library(raster)
library(plyr)
library(dplyr)
library(ggplot2)
library(viridisLite)
library(colorspace)
library(RColorBrewer)
library(sf)
library(stringr)
library(ggpubr)
library(kableExtra)
memory.limit(size = 160000)
## [1] Inf
#display.brewer.pal(n = 11, name ="RdYlGn")
colorvec <- brewer.pal(n = 11, name ="RdYlGn")
col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")
land_mask <- raster("Masks/land_sea_mask_1degree.nc4")
land_mask.df <- as.data.frame(land_mask, xy = T) %>% setNames(c("lon","lat","lm")) # 0 if ocean, 1 if land
elev <- raster("Worldclim/wc2.1_10m/wc2.1_10m_elev.tif")
elev.df <- elev %>% projectRaster(to = land_mask) %>% as.data.frame(xy = T) %>% setNames(c("lon","lat", "z"))
ipcc_regions <- shapefile("Masks/IPCC-WGI-reference-regions-v4.shp") %>% spTransform(crs("EPSG:4326"))
ipcc_regions.raster <- ipcc_regions %>% rasterize(land_mask)
ipcc_regions.df <- as.data.frame(ipcc_regions.raster, xy = T) %>% setNames(c("lon","lat","Continent","Type","Name","Acronym"))
tas.all_annual <- read.table("CMIP6/tas/tas.all_annual.txt")
pr.all_annual <- read.table("CMIP6/pr/pr.all_annual.txt")
sfcWind.all_annual <- read.table("CMIP6/sfcWind/sfcWind.all_annual.txt")
hfls.all_annual <- read.table("CMIP6/hfls/hfls.all_annual.txt")
hfss.all_annual <- read.table("CMIP6/hfss/hfss.all_annual.txt")
hurs.all_annual <- read.table("CMIP6/hurs/hurs.all_annual.txt")
cmip6_annual <- tas.all_annual %>% merge(pr.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_annual, "cmip6_annual.txt")
tas.all_january <- read.table("CMIP6/tas/tas.all_january.txt")
pr.all_january <- read.table("CMIP6/pr/pr.all_january.txt")
sfcWind.all_january <- read.table("CMIP6/sfcWind/sfcWind.all_january.txt")
hfls.all_january <- read.table("CMIP6/hfls/hfls.all_january.txt")
hfss.all_january <- read.table("CMIP6/hfss/hfss.all_january.txt")
hurs.all_january <- read.table("CMIP6/hurs/hurs.all_january.txt")
cmip6_january <- tas.all_january %>% merge(pr.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_january, "cmip6_january.txt")
tas.all_february <- read.table("CMIP6/tas/tas.all_february.txt")
pr.all_february <- read.table("CMIP6/pr/pr.all_february.txt")
sfcWind.all_february <- read.table("CMIP6/sfcWind/sfcWind.all_february.txt")
hfls.all_february <- read.table("CMIP6/hfls/hfls.all_february.txt")
hfss.all_february <- read.table("CMIP6/hfss/hfss.all_february.txt")
hurs.all_february <- read.table("CMIP6/hurs/hurs.all_february.txt")
cmip6_february <- tas.all_february %>% merge(pr.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_february, "cmip6_february.txt")
tas.all_march <- read.table("CMIP6/tas/tas.all_march.txt")
pr.all_march <- read.table("CMIP6/pr/pr.all_march.txt")
sfcWind.all_march <- read.table("CMIP6/sfcWind/sfcWind.all_march.txt")
hfls.all_march <- read.table("CMIP6/hfls/hfls.all_march.txt")
hfss.all_march <- read.table("CMIP6/hfss/hfss.all_march.txt")
hurs.all_march <- read.table("CMIP6/hurs/hurs.all_march.txt")
cmip6_march <- tas.all_march %>% merge(pr.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_march, "cmip6_march.txt")
tas.all_april <- read.table("CMIP6/tas/tas.all_april.txt")
pr.all_april <- read.table("CMIP6/pr/pr.all_april.txt")
sfcWind.all_april <- read.table("CMIP6/sfcWind/sfcWind.all_april.txt")
hfls.all_april <- read.table("CMIP6/hfls/hfls.all_april.txt")
hfss.all_april <- read.table("CMIP6/hfss/hfss.all_april.txt")
hurs.all_april <- read.table("CMIP6/hurs/hurs.all_april.txt")
cmip6_april <- tas.all_april %>% merge(pr.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_april, "cmip6_april.txt")
tas.all_may <- read.table("CMIP6/tas/tas.all_may.txt")
pr.all_may <- read.table("CMIP6/pr/pr.all_may.txt")
sfcWind.all_may <- read.table("CMIP6/sfcWind/sfcWind.all_may.txt")
hfls.all_may <- read.table("CMIP6/hfls/hfls.all_may.txt")
hfss.all_may <- read.table("CMIP6/hfss/hfss.all_may.txt")
hurs.all_may <- read.table("CMIP6/hurs/hurs.all_may.txt")
cmip6_may <- tas.all_may %>% merge(pr.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_may, "cmip6_may.txt")
tas.all_june <- read.table("CMIP6/tas/tas.all_june.txt")
pr.all_june <- read.table("CMIP6/pr/pr.all_june.txt")
sfcWind.all_june <- read.table("CMIP6/sfcWind/sfcWind.all_june.txt")
hfls.all_june <- read.table("CMIP6/hfls/hfls.all_june.txt")
hfss.all_june <- read.table("CMIP6/hfss/hfss.all_june.txt")
hurs.all_june <- read.table("CMIP6/hurs/hurs.all_june.txt")
cmip6_june <- tas.all_june %>% merge(pr.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_june, "cmip6_june.txt")
tas.all_july <- read.table("CMIP6/tas/tas.all_july.txt")
pr.all_july <- read.table("CMIP6/pr/pr.all_july.txt")
sfcWind.all_july <- read.table("CMIP6/sfcWind/sfcWind.all_july.txt")
hfls.all_july <- read.table("CMIP6/hfls/hfls.all_july.txt")
hfss.all_july <- read.table("CMIP6/hfss/hfss.all_july.txt")
hurs.all_july <- read.table("CMIP6/hurs/hurs.all_july.txt")
cmip6_july <- tas.all_july %>% merge(pr.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_july, "cmip6_july.txt")
tas.all_august <- read.table("CMIP6/tas/tas.all_august.txt")
pr.all_august <- read.table("CMIP6/pr/pr.all_august.txt")
sfcWind.all_august <- read.table("CMIP6/sfcWind/sfcWind.all_august.txt")
hfls.all_august <- read.table("CMIP6/hfls/hfls.all_august.txt")
hfss.all_august <- read.table("CMIP6/hfss/hfss.all_august.txt")
hurs.all_august <- read.table("CMIP6/hurs/hurs.all_august.txt")
cmip6_august <- tas.all_august %>% merge(pr.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_august, "cmip6_august.txt")
tas.all_september <- read.table("CMIP6/tas/tas.all_september.txt")
pr.all_september <- read.table("CMIP6/pr/pr.all_september.txt")
sfcWind.all_september <- read.table("CMIP6/sfcWind/sfcWind.all_september.txt")
hfls.all_september <- read.table("CMIP6/hfls/hfls.all_september.txt")
hfss.all_september <- read.table("CMIP6/hfss/hfss.all_september.txt")
hurs.all_september <- read.table("CMIP6/hurs/hurs.all_september.txt")
cmip6_september <- tas.all_september %>% merge(pr.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_september, "cmip6_september.txt")
tas.all_october <- read.table("CMIP6/tas/tas.all_october.txt")
pr.all_october <- read.table("CMIP6/pr/pr.all_october.txt")
sfcWind.all_october <- read.table("CMIP6/sfcWind/sfcWind.all_october.txt")
hfls.all_october <- read.table("CMIP6/hfls/hfls.all_october.txt")
hfss.all_october <- read.table("CMIP6/hfss/hfss.all_october.txt")
hurs.all_october <- read.table("CMIP6/hurs/hurs.all_october.txt")
cmip6_october <- tas.all_october %>% merge(pr.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_october, "cmip6_october.txt")
tas.all_november <- read.table("CMIP6/tas/tas.all_november.txt")
pr.all_november <- read.table("CMIP6/pr/pr.all_november.txt")
sfcWind.all_november <- read.table("CMIP6/sfcWind/sfcWind.all_november.txt")
hfls.all_november <- read.table("CMIP6/hfls/hfls.all_november.txt")
hfss.all_november <- read.table("CMIP6/hfss/hfss.all_november.txt")
hurs.all_november <- read.table("CMIP6/hurs/hurs.all_november.txt")
cmip6_november <- tas.all_november %>% merge(pr.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_november, "cmip6_november.txt")
tas.all_december <- read.table("CMIP6/tas/tas.all_december.txt")
pr.all_december <- read.table("CMIP6/pr/pr.all_december.txt")
sfcWind.all_december <- read.table("CMIP6/sfcWind/sfcWind.all_december.txt")
hfls.all_december <- read.table("CMIP6/hfls/hfls.all_december.txt")
hfss.all_december <- read.table("CMIP6/hfss/hfss.all_december.txt")
hurs.all_december <- read.table("CMIP6/hurs/hurs.all_december.txt")
cmip6_december <- tas.all_december %>% merge(pr.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_december, "cmip6_december.txt")
cmip6 <- read.table("cmip6_annual.txt")
breaks.martonne <- c(0,5,10,20,"30","40",Inf)
cat.martonne <- c("[0,5]" = "Desert", "(5,10]"= "Arid", "(10,20]" = "Semi-arid","(20,30]" = "Temperate", "(30,40]" = "Humid", "(40,Inf]" = "Forest")
col.martonne <- c("Desert" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Temperate" = colorvec[8], "Humid"= colorvec[10],"Forest"= colorvec[11])
cmip6$AIm <- with(cmip6,pr*60*60*24*365/(tas-273.15+10)) # pr in mm/y, tas in Ceslsius
cmip6$cat.AIm <- cmip6$AIm %>% cut(breaks.martonne, include.lowest = T) %>% revalue(cat.martonne)
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AIm))+
scale_fill_manual(values = col.martonne, na.translate = F)+
labs(title = paste(i, "1970-2000", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
ea from Rh:
ea = 6.108(RH/100)exp(17.27*T/(T+237.3)) Refs https://www.nature.com/articles/s41598-023-40499-6 : Allen and Tetens
id_vars <- c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")
cmip6_annual <- mutate(read.table("cmip6_annual.txt"),
t = tas - 273.15,
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_annual = ((0.408*delta*Rn+gamma*900/((t)+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_01 <- mutate(read.table("cmip6_january.txt"),
t = tas - 273.15,
pr_01 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_01 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind)))) # Evapotranspiration, G considered to be negligible. in mm/day. Wind speed at 10 m is converted to wind speed at 2 m by multiplying by 0.748
cmip6_02 <- mutate(read.table("cmip6_february.txt"),
t = tas - 273.15,
pr_02 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_02 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_03 <- mutate(read.table("cmip6_march.txt"),
t = tas - 273.15,
pr_03 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_03 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_04 <- mutate(read.table("cmip6_april.txt"),
t = tas - 273.15,
pr_04 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_04 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_05 <- mutate(read.table("cmip6_may.txt"),
t = tas - 273.15,
pr_05 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_05 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_06 <- mutate(read.table("cmip6_june.txt"),
t = tas - 273.15,
pr_06 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_06 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_07 <- mutate(read.table("cmip6_july.txt"),
t = tas - 273.15,
pr_07 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_07 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_08 <- mutate(read.table("cmip6_august.txt"),
t = tas - 273.15,
pr_08 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_08 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_09 <- mutate(read.table("cmip6_september.txt"),
t = tas - 273.15,
pr_09 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_09 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_10 <- mutate(read.table("cmip6_october.txt"),
t = tas - 273.15,
pr_10 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_10 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_11 <- mutate(read.table("cmip6_november.txt"),
t = tas - 273.15,
pr_11 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_11 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_12 <- mutate(read.table("cmip6_december.txt"),
t = tas - 273.15,
pr_12 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_12 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
et0_monthly <- select(cmip6_annual, id_vars) %>%
merge(select(cmip6_01, c(id_vars, "ET0_01", "pr_01")), by = id_vars) %>%
merge(select(cmip6_02, c(id_vars, "ET0_02", "pr_02")), by = id_vars) %>%
merge(select(cmip6_03, c(id_vars, "ET0_03", "pr_03")), by = id_vars) %>%
merge(select(cmip6_04, c(id_vars, "ET0_04", "pr_04")), by = id_vars) %>%
merge(select(cmip6_05, c(id_vars, "ET0_05", "pr_05")), by = id_vars) %>%
merge(select(cmip6_06, c(id_vars, "ET0_06", "pr_06")), by = id_vars) %>%
merge(select(cmip6_07, c(id_vars, "ET0_07", "pr_07")), by = id_vars) %>%
merge(select(cmip6_08, c(id_vars, "ET0_08", "pr_08")), by = id_vars) %>%
merge(select(cmip6_09, c(id_vars, "ET0_09", "pr_09")), by = id_vars) %>%
merge(select(cmip6_10, c(id_vars, "ET0_10", "pr_10")), by = id_vars) %>%
merge(select(cmip6_11, c(id_vars, "ET0_11", "pr_11")), by = id_vars) %>%
merge(select(cmip6_12, c(id_vars, "ET0_12", "pr_12")), by = id_vars)
et0_monthly$spr <- with(et0_monthly, pr_01*31 + pr_02 * 28 + pr_03 * 31 + pr_04 * 30 + pr_05 * 31 + pr_06 * 30 +
pr_07 * 31 + pr_08 * 31 + pr_09 * 30 + pr_10 * 31 + pr_11 * 30 + pr_12 * 31)
et0_monthly$sET0 <- with(et0_monthly, ET0_01*31 + ET0_02 * 28 + ET0_03 * 31 + ET0_04 * 30 + ET0_05 * 31 + ET0_06 * 30 +
ET0_07 * 31 + ET0_08 * 31 + ET0_09 * 30 + ET0_10 * 31 + ET0_11 * 30 + ET0_12 * 31)
cmip6 <- merge(cmip6_annual, et0_monthly, by = id_vars)
cmip6$AI <- with(cmip6, abs(spr/sET0))
cmip6$AI_annual <- with(cmip6, abs((pr*60*60*24*365)/(ET0_annual*365)))
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6$cat.AI[which(cmip6$sET0 < 400)] <- "Cold"
cmip6$cat.AI %>% unique()
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual <- cmip6$AI_annual %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual[which(cmip6$ET0_annual*365 < 400)] <- "Cold"
write.table(cmip6, "cmip6_AI.txt")
cmip6 <- read.table("cmip6_AI.txt")
breaks.unesco <- c(-Inf,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(-Inf,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid")
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1850_1880" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1850-1880", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.1 | 9.0 | 13.2 | 6.1 | 33.4 | 38.0 | 28.3 | 0.3 |
| CESM | 5.2 | 10.8 | 14.7 | 5.0 | 35.7 | 35.6 | 28.5 | 0.3 |
| CMCC | 5.6 | 7.2 | 9.5 | 5.3 | 27.6 | 42.1 | 30.1 | 0.3 |
| CMCC-ESM2 | 6.0 | 8.2 | 7.5 | 2.8 | 24.5 | 24.0 | 51.2 | 0.3 |
| CNRM | 5.7 | 12.1 | 11.4 | 6.6 | 35.8 | 38.0 | 25.9 | 0.2 |
| EC-Earth3 | 8.6 | 9.0 | 11.0 | 4.5 | 33.1 | 36.7 | 30.1 | 0.2 |
| FGOALS | 5.6 | 12.8 | 15.9 | 7.2 | 41.5 | 29.9 | 28.4 | 0.2 |
| GFDL-ESM4 | 5.6 | 9.3 | 8.7 | 4.1 | 27.7 | 37.0 | 34.9 | 0.2 |
| INM | 4.4 | 8.2 | 13.4 | 5.8 | 31.8 | 40.6 | 27.4 | 0.3 |
| INM-CM5 | 3.5 | 8.8 | 11.5 | 5.6 | 29.4 | 43.2 | 27.1 | 0.3 |
| MPI | 8.5 | 10.9 | 10.1 | 4.4 | 33.9 | 34.5 | 31.4 | 0.2 |
| MRI | 6.3 | 10.6 | 8.7 | 3.8 | 29.4 | 39.6 | 30.9 | 0.3 |
| NorESM-2-MM | 5.1 | 10.4 | 18.4 | 5.9 | 39.8 | 35.4 | 24.4 | 0.3 |
| mean | 5.8 | 9.8 | 11.8 | 5.2 | 32.6 | 36.5 | 30.7 | 0.3 |
| sd | 1.4 | 1.6 | 3.2 | 1.2 | 7.4 | 5.1 | 6.7 | 0.1 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1850-1880", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
library(ggrepel)
pie <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(c("source", "cat.AI")) %>%
reshape2::melt(id.vars = c("source")) %>%
group_by(source, value) %>%
summarise(count = n()) %>%
mutate(perc = round(count/sum(count)*100, 1),
lab.y = rev(cumsum(rev(count))) - count*0.5) %>%
ungroup()
pie_list <- list()
for(i in unique(pie$source)){
p <- ggplot(subset(pie, source == i))+
geom_bar(aes(x = "", y = count, fill = value), width = 1, stat = "identity")+
coord_polar("y", start = 0)+
scale_fill_manual(values = col.cat)+
geom_label_repel(aes(x = "", y = lab.y, label = perc), nudge_x = 0.5)+
labs(fill = "", title = i)+
theme_void()+theme(legend.position = "right")
pie_list[[i]] <- p
}
ggpubr::ggarrange(plotlist = pie_list, nrow = 4, ncol = 4, common.legend = T, legend = "bottom")
pie <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(c("cat.AI")) %>%
table() %>% as.data.frame() %>%
mutate(perc = round(Freq/sum(Freq)*100, 1),
lab.y = rev(cumsum(rev(Freq))) - Freq*0.5)
p <- ggplot(pie)+
geom_bar(aes(x = "", y = Freq, fill = cat.AI), width = 1, stat = "identity")+
coord_polar("y", start = 0)+
scale_fill_manual(values = col.cat)+
geom_label_repel(aes(x = "", y = lab.y, label = perc), nudge_x = 0.5)+
labs(fill = "", title = "Multimodel CMIP6")+
theme_void()+theme(legend.position = "right")
print(p)
sdmm <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.2 | 8.6 | 13.2 | 6.3 | 33.3 | 37.4 | 29.1 | 0.3 |
| CESM | 5.3 | 10.7 | 14.1 | 5.2 | 35.3 | 35.0 | 29.5 | 0.3 |
| CMCC | 5.1 | 7.4 | 10.1 | 5.4 | 28.0 | 42.2 | 29.5 | 0.3 |
| CMCC-ESM2 | 6.4 | 7.9 | 7.7 | 2.5 | 24.5 | 24.4 | 50.8 | 0.3 |
| CNRM | 5.8 | 12.2 | 11.3 | 6.5 | 35.8 | 38.3 | 25.7 | 0.2 |
| EC-Earth3 | 8.4 | 9.5 | 10.8 | 4.4 | 33.1 | 35.7 | 31.0 | 0.2 |
| FGOALS | 5.9 | 10.9 | 10.0 | 4.9 | 31.7 | 25.1 | 42.9 | 0.2 |
| GFDL-ESM4 | 5.8 | 8.9 | 8.9 | 4.1 | 27.7 | 35.3 | 36.8 | 0.2 |
| INM | 4.4 | 8.4 | 13.3 | 5.7 | 31.8 | 40.1 | 27.8 | 0.3 |
| INM-CM5 | 3.4 | 8.5 | 12.0 | 5.6 | 29.5 | 43.0 | 27.3 | 0.3 |
| MPI | 8.7 | 11.1 | 10.2 | 4.3 | 34.3 | 34.0 | 31.5 | 0.2 |
| MRI | 6.1 | 10.8 | 9.2 | 3.9 | 30.0 | 38.9 | 30.9 | 0.3 |
| NorESM-2-MM | 5.2 | 10.1 | 17.1 | 5.6 | 38.0 | 33.4 | 28.3 | 0.3 |
| mean | 5.8 | 9.6 | 11.4 | 5.0 | 31.8 | 35.6 | 32.4 | 0.3 |
| sd | 1.4 | 1.5 | 2.5 | 1.1 | 6.5 | 5.7 | 7.1 | 0.1 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1985_2015" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1985-2010", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.3 | 8.4 | 13.8 | 6.6 | 34.1 | 37.2 | 28.3 | 0.3 |
| CESM | 4.8 | 10.8 | 14.8 | 5.4 | 35.8 | 35.4 | 28.5 | 0.3 |
| CMCC | 5.2 | 7.6 | 10.4 | 5.8 | 29.0 | 42.9 | 27.9 | 0.3 |
| CMCC-ESM2 | 6.2 | 8.5 | 7.7 | 2.5 | 24.9 | 24.5 | 50.3 | 0.3 |
| CNRM | 5.8 | 12.1 | 11.3 | 6.2 | 35.4 | 39.2 | 25.2 | 0.2 |
| EC-Earth3 | 8.3 | 9.8 | 10.7 | 4.4 | 33.2 | 36.7 | 29.9 | 0.2 |
| FGOALS | 5.8 | 11.4 | 10.4 | 4.9 | 32.5 | 25.3 | 42.0 | 0.2 |
| GFDL-ESM4 | 5.9 | 9.1 | 9.1 | 3.9 | 28.0 | 36.5 | 35.3 | 0.2 |
| INM | 4.0 | 7.7 | 11.3 | 5.2 | 28.2 | 36.5 | 35.0 | 0.3 |
| INM-CM5 | 3.5 | 8.3 | 12.0 | 5.4 | 29.2 | 40.6 | 29.9 | 0.3 |
| MPI | 8.7 | 11.1 | 10.3 | 4.4 | 34.5 | 34.1 | 31.0 | 0.2 |
| MRI | 6.2 | 10.7 | 9.1 | 3.8 | 29.8 | 38.4 | 31.6 | 0.3 |
| NorESM-2-MM | 4.7 | 9.9 | 15.9 | 5.6 | 36.1 | 30.3 | 33.2 | 0.3 |
| mean | 5.7 | 9.6 | 11.3 | 4.9 | 31.5 | 35.2 | 32.9 | 0.3 |
| sd | 1.5 | 1.5 | 2.3 | 1.1 | 6.4 | 5.5 | 6.7 | 0.1 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.0 | 9.2 | 14.6 | 6.5 | 35.3 | 38.1 | 26.2 | 0.3 |
| CESM | 4.7 | 12.4 | 15.3 | 5.6 | 38.0 | 35.6 | 26.2 | 0.3 |
| CMCC | 4.4 | 8.4 | 11.0 | 6.1 | 29.9 | 44.3 | 25.5 | 0.3 |
| CMCC-ESM2 | 6.1 | 9.1 | 8.0 | 2.8 | 26.0 | 25.3 | 48.4 | 0.3 |
| CNRM | 6.0 | 12.7 | 12.1 | 6.8 | 37.6 | 39.8 | 22.3 | 0.2 |
| EC-Earth3 | 7.5 | 9.2 | 11.1 | 4.5 | 32.3 | 36.3 | 31.2 | 0.2 |
| FGOALS | 6.9 | 13.3 | 18.5 | 7.8 | 46.5 | 30.3 | 22.9 | 0.2 |
| GFDL-ESM4 | 5.4 | 10.1 | 9.5 | 4.4 | 29.4 | 36.9 | 33.4 | 0.2 |
| INM | 4.6 | 8.4 | 13.8 | 5.9 | 32.7 | 40.4 | 26.7 | 0.3 |
| INM-CM5 | 3.5 | 8.6 | 12.2 | 5.4 | 29.7 | 43.1 | 26.9 | 0.3 |
| MPI | 9.0 | 11.9 | 11.1 | 4.5 | 36.5 | 35.1 | 28.2 | 0.2 |
| MRI | 6.2 | 11.2 | 9.3 | 4.1 | 30.8 | 40.0 | 28.9 | 0.3 |
| NorESM-2-MM | 5.3 | 11.8 | 16.9 | 5.6 | 39.6 | 33.5 | 26.7 | 0.3 |
| mean | 4.0 | 7.3 | 8.7 | 3.7 | 23.7 | 25.6 | 48.0 | 2.6 |
| sd | 1.0 | 1.2 | 2.2 | 0.9 | 5.3 | 3.6 | 4.6 | 0.0 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.8 | 10.5 | 15.0 | 7.3 | 37.6 | 38.2 | 24.1 | 0.3 |
| CESM | 6.1 | 11.6 | 15.4 | 5.4 | 38.5 | 36.9 | 24.3 | 0.3 |
| CMCC | 4.3 | 8.0 | 12.3 | 6.4 | 31.0 | 46.6 | 22.1 | 0.3 |
| CMCC-ESM2 | 6.2 | 9.5 | 8.5 | 2.8 | 27.0 | 26.8 | 46.0 | 0.3 |
| CNRM | 6.3 | 12.9 | 12.4 | 6.8 | 38.4 | 41.0 | 20.4 | 0.2 |
| EC-Earth3 | 7.6 | 9.1 | 11.0 | 4.4 | 32.1 | 37.3 | 30.3 | 0.2 |
| FGOALS | 6.9 | 14.0 | 18.1 | 8.1 | 47.1 | 30.3 | 22.3 | 0.2 |
| GFDL-ESM4 | 5.9 | 9.7 | 9.8 | 4.4 | 29.8 | 38.4 | 31.6 | 0.2 |
| INM | 4.8 | 8.2 | 14.1 | 5.8 | 32.9 | 40.6 | 26.2 | 0.3 |
| INM-CM5 | 3.7 | 9.5 | 11.2 | 5.9 | 30.3 | 43.5 | 25.9 | 0.3 |
| MPI | 9.0 | 11.9 | 11.1 | 4.5 | 36.5 | 35.1 | 28.2 | 0.2 |
| MRI | 6.5 | 11.0 | 9.6 | 4.0 | 31.1 | 40.8 | 27.9 | 0.3 |
| NorESM-2-MM | 5.9 | 11.7 | 17.0 | 5.9 | 40.5 | 34.0 | 25.3 | 0.3 |
| mean | 4.2 | 7.4 | 8.8 | 3.8 | 24.2 | 26.2 | 47.0 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.5 | 0.0 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.9 | 9.6 | 14.3 | 6.8 | 35.6 | 37.4 | 26.8 | 0.3 |
| CESM | 5.2 | 11.4 | 15.6 | 5.4 | 37.6 | 35.7 | 26.5 | 0.3 |
| CMCC | 4.2 | 8.3 | 11.3 | 5.9 | 29.7 | 44.1 | 25.9 | 0.3 |
| CMCC-ESM2 | 3.3 | 8.4 | 9.0 | 3.6 | 24.3 | 26.9 | 48.6 | 0.3 |
| CNRM | 6.2 | 12.9 | 12.9 | 7.1 | 39.1 | 39.6 | 21.0 | 0.2 |
| EC-Earth3 | 7.9 | 9.7 | 11.6 | 4.8 | 34.0 | 38.5 | 27.3 | 0.2 |
| FGOALS | 6.1 | 11.9 | 11.9 | 5.3 | 35.2 | 27.5 | 37.0 | 0.2 |
| GFDL-ESM4 | 6.3 | 9.4 | 9.9 | 4.1 | 29.7 | 35.9 | 34.1 | 0.2 |
| INM | 4.6 | 8.3 | 13.5 | 5.9 | 32.3 | 40.3 | 27.1 | 0.3 |
| INM-CM5 | 3.4 | 8.8 | 12.1 | 5.6 | 29.9 | 43.2 | 26.5 | 0.3 |
| MPI | 8.7 | 12.2 | 11.0 | 4.5 | 36.4 | 36.4 | 26.9 | 0.2 |
| MRI | 6.5 | 10.8 | 9.4 | 3.6 | 30.3 | 40.2 | 29.2 | 0.3 |
| NorESM-2-MM | 5.4 | 11.0 | 17.9 | 5.7 | 40.0 | 33.2 | 26.5 | 0.3 |
| mean | 3.9 | 7.1 | 8.6 | 3.7 | 23.3 | 25.6 | 48.5 | 2.6 |
| sd | 1.1 | 1.1 | 1.8 | 0.8 | 4.8 | 3.6 | 4.8 | 0.0 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.5 | 10.5 | 15.4 | 6.9 | 38.3 | 37.3 | 24.2 | 0.3 |
| CESM | 6.0 | 12.2 | 15.6 | 5.7 | 39.5 | 37.4 | 22.9 | 0.3 |
| CMCC | 5.2 | 9.1 | 12.5 | 7.3 | 34.1 | 44.1 | 21.6 | 0.3 |
| CMCC-ESM2 | 6.9 | 9.9 | 8.1 | 3.0 | 27.9 | 25.9 | 45.9 | 0.3 |
| CNRM | 7.3 | 12.9 | 13.2 | 6.7 | 40.1 | 39.3 | 20.3 | 0.2 |
| EC-Earth3 | 7.6 | 9.8 | 12.4 | 4.7 | 34.5 | 40.3 | 24.9 | 0.2 |
| FGOALS | 6.7 | 13.3 | 13.3 | 4.4 | 37.7 | 28.0 | 34.1 | 0.2 |
| GFDL-ESM4 | 7.5 | 8.5 | 10.9 | 4.7 | 31.6 | 37.0 | 31.1 | 0.2 |
| INM | 6.0 | 8.2 | 14.7 | 6.1 | 35.0 | 39.2 | 25.5 | 0.3 |
| INM-CM5 | 3.9 | 10.3 | 14.1 | 6.1 | 34.4 | 40.4 | 24.9 | 0.3 |
| MPI | 8.7 | 12.2 | 11.0 | 4.5 | 36.4 | 36.4 | 26.9 | 0.2 |
| MRI | 7.2 | 11.9 | 9.9 | 4.1 | 33.1 | 39.0 | 27.6 | 0.3 |
| NorESM-2-MM | 5.9 | 11.8 | 17.5 | 5.9 | 41.1 | 33.7 | 24.9 | 0.3 |
| mean | 4.5 | 7.5 | 9.0 | 3.8 | 24.8 | 25.6 | 47.0 | 2.6 |
| sd | 0.9 | 1.2 | 1.8 | 0.9 | 4.8 | 3.5 | 4.7 | 0.0 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.8 | 9.2 | 15.0 | 7.5 | 36.5 | 37.6 | 25.6 | 0.3 |
| CESM | 5.8 | 11.5 | 15.6 | 5.2 | 38.1 | 36.0 | 25.7 | 0.3 |
| CMCC | 4.4 | 8.0 | 11.5 | 6.5 | 30.4 | 44.8 | 24.5 | 0.3 |
| CMCC-ESM2 | 6.5 | 9.3 | 8.0 | 2.5 | 26.3 | 25.4 | 48.0 | 0.3 |
| CNRM | 6.2 | 13.3 | 12.4 | 6.9 | 38.8 | 40.0 | 21.0 | 0.2 |
| EC-Earth3 | 7.8 | 9.4 | 11.8 | 4.6 | 33.6 | 38.8 | 27.4 | 0.2 |
| FGOALS | 6.8 | 13.5 | 18.1 | 7.7 | 46.1 | 30.8 | 22.8 | 0.2 |
| GFDL-ESM4 | 6.1 | 9.4 | 9.7 | 4.7 | 29.9 | 36.4 | 33.4 | 0.2 |
| INM | 4.6 | 8.4 | 14.0 | 6.0 | 33.0 | 40.6 | 26.2 | 0.3 |
| INM-CM5 | 3.5 | 8.6 | 12.4 | 5.8 | 30.3 | 43.6 | 25.8 | 0.3 |
| MPI | 8.6 | 12.4 | 11.1 | 4.5 | 36.6 | 37.0 | 26.2 | 0.2 |
| MRI | 6.0 | 11.1 | 9.6 | 4.0 | 30.7 | 40.8 | 28.1 | 0.3 |
| NorESM-2-MM | 5.5 | 11.4 | 17.6 | 5.9 | 40.4 | 33.2 | 26.2 | 0.3 |
| mean | 4.1 | 7.3 | 8.9 | 3.8 | 24.1 | 26.0 | 47.3 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.7 | 0.0 |
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(13,14), bold = T)
| source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 6.1 | 10.8 | 15.9 | 6.5 | 39.3 | 37.5 | 22.9 | 0.3 |
| CMCC | 4.4 | 9.3 | 13.5 | 6.7 | 33.9 | 46.7 | 19.2 | 0.3 |
| CMCC-ESM2 | 5.6 | 10.5 | 8.9 | 2.9 | 27.9 | 28.1 | 43.8 | 0.3 |
| CNRM | 6.0 | 13.6 | 12.8 | 6.4 | 38.8 | 40.9 | 20.1 | 0.2 |
| EC-Earth3 | 7.7 | 9.6 | 13.0 | 4.9 | 35.2 | 40.5 | 23.9 | 0.2 |
| FGOALS | 15.6 | 15.0 | 18.3 | 6.8 | 55.7 | 24.4 | 19.8 | 0.2 |
| GFDL-ESM4 | 6.5 | 10.0 | 10.8 | 4.8 | 32.1 | 37.6 | 30.1 | 0.2 |
| INM | 4.4 | 8.7 | 14.8 | 6.0 | 33.9 | 41.7 | 24.1 | 0.3 |
| INM-CM5 | 3.9 | 9.8 | 12.0 | 6.0 | 31.7 | 44.1 | 23.9 | 0.3 |
| MPI | 8.7 | 12.3 | 11.1 | 4.7 | 36.8 | 36.8 | 26.2 | 0.2 |
| MRI | 7.1 | 11.5 | 10.6 | 4.1 | 33.3 | 40.1 | 26.4 | 0.3 |
| NorESM-2-MM | 6.4 | 12.8 | 17.4 | 6.5 | 43.1 | 34.3 | 22.3 | 0.3 |
| mean | 4.8 | 7.8 | 9.2 | 3.8 | 25.6 | 26.2 | 45.5 | 2.6 |
| sd | 2.1 | 1.3 | 2.0 | 0.8 | 6.2 | 4.4 | 4.6 | 0.0 |
test <- cmip6ext %>% subset(model %in% c("historical","SSP585") & period %in% c("1970_2000", "2070_2100") & lon == 25.5 & lat == 10.5) %>% select(c("source","lon","lat","model","period","AI","cat.AI","AI.ref"))
test2 <- cmip6s %>% subset(model == "SSP585" & period == "2070_2100" & lon == 25.5 & lat == 10.5) %>%
select(c("model","period","AI.mean","cat.AI","AI.ref.mean","diff.AI.mean", "nb.models", "diff.AI.mean"))
test2
cmip6 <- read.table("cmip6_AI.txt")
cmip6.ref <- cmip6 %>% filter(period == "1970_2000") %>% select(c("lon","lat","model","period","source","AI","spr","t")) %>% setNames(c("lon","lat","model","period","source","AI.ref","spr.ref","t.ref"))
cmip6ext <- merge(cmip6, select(cmip6.ref, c("lon","lat","source","AI.ref","spr.ref","t.ref")) , by = c("lon","lat","source"), all = T) %>% mutate(diff.AI = AI - AI.ref, diff.pr = spr - spr.ref, diff.t = t - t.ref)
write.table(cmip6ext, "cmip6ext.txt")
cmip6 <- read.table("cmip6ext.txt")
cmip6s <- cmip6 %>%
group_by(lon,lat,Continent, Type, Name, Acronym, lm, model, period, z) %>%
dplyr::summarise(t.mean = mean(t, na.rm = T), t.sd = sd(t, na.rm = T), # t in °C
pr.mean = mean(spr, na.rm = T) , pr.sd = sd(spr, na.rm = T), # pr in mm/y
sfcWind.mean = mean(sfcWind, na.rm = T), sfcWind.sd = sd(sfcWind, na.rm = T),
Rn.mean = mean(Rn, na.rm = T), Rn.sd = sd(Rn, na.rm = T), # in MJ/m2/day
ea.mean = mean(ea, na.rm = T), ea.sd = sd(ea, na.rm = T), # in kPa
ET0 = mean(sET0, na.rm = T), ET0.sd = sd(sET0, na.rm = T), # in mm/y
AI.mean = mean(AI, na.rm = T), AI.sd = sd(AI, na.rm = T),
AI.ref.mean = mean(AI.ref, na.rm = T), AI.ref.sd = sd(AI.ref, na.rm = T),
diff.AI.mean = mean(diff.AI, na.rm = T), diff.AI.sd = sd(diff.AI, na.rm = T),
spr.ref.mean = mean(spr.ref, na.rm = T), spr.ref.sd = sd(spr.ref, na.rm = T),
diff.pr.mean = mean(diff.pr, na.rm = T), diff.pr.sd = sd(diff.pr, na.rm = T),
t.ref.mean = mean(t.ref, na.rm = T), t.ref.sd = sd(t.ref, na.rm =T),
diff.t.mean = mean(diff.t, na.rm = T), diff.t.sd = sd(diff.t, na.rm = T),
Hyperarid = sum(cat.AI == "Hyperarid"),
Arid = sum(cat.AI == "Arid"),
'Semi-arid' = sum(cat.AI == "Semi-arid"),
'Dry subhumid' = sum(cat.AI == "Dry subhumid") ,
'Humid' = sum(cat.AI == "Humid"),
'Cold' = sum(cat.AI == "Cold"),
'NA' = sum(is.na(cat.AI)),
nb.models = n(),
plus.AI = table(diff.AI > 0)["TRUE"],
minus.AI = table(diff.AI < 0)["TRUE"]) %>%
ungroup() %>% as.data.frame()
cmip6s$cat.maj <- colnames(select(cmip6s, c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")))[max.col(select(cmip6s, c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")),ties.method = "first")]
cmip6s$nb.maj <- cmip6s %>% select(c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")) %>% apply(1, max)
cmip6s$prop.maj <- with(cmip6s, nb.maj / nb.models)
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
cmip6s$cat.AI <- cmip6s$AI.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI[which(cmip6s$ET0 < 400)] <- "Cold"
cmip6s$cat.AI.ref <- cmip6s$AI.ref.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI.ref[which(cmip6s$ET0 < 400)] <- "Cold"
cmip6s$same.catAI <- with(cmip6s, ifelse(cat.maj == cat.AI, "same", "diff"))
cmip6s$cat.dryer <- with(cmip6s,
ifelse(cat.AI.ref == "Arid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(cat.AI.ref == "Arid" & cat.AI == "Hyperarid", "dryer",
ifelse(cat.AI.ref == "Semi-arid" & cat.AI %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(cat.AI.ref == "Semi-arid" & cat.AI %in% c("Hyperarid","Arid"), "dryer",
ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Cold","Humid"), "wetter",
ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Humid" & cat.AI == "Cold","wetter",
ifelse(cat.AI.ref == "Humid" & cat.AI %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Cold" & cat.AI %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Hyperarid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
ifelse(cat.AI.ref == cat.AI, "same",
NA))))))))))))
cmip6sref <- cmip6s %>% filter(period == "1970_2000") %>% select(c("lon","lat","model","period","cat.maj")) %>% setNames(c("lon","lat","model","period","cat.maj.ref"))
cmip6s <- merge(cmip6s, select(cmip6sref, c("lon","lat", "model","period","cat.maj.ref")),
by = c("lon","lat","model","period"), all = T)
cmip6s$cat.maj.dryer <- with(cmip6s,
ifelse(cat.maj.ref == "Arid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(cat.maj.ref == "Arid" & cat.maj == "Hyperarid", "dryer",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj %in% c("Hyperarid","Arid"), "dryer",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Cold","Humid"), "wetter",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Humid" & cat.maj == "Cold","wetter",
ifelse(cat.maj.ref == "Humid" & cat.maj %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Cold" & cat.maj %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Hyperarid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
ifelse(cat.maj.ref == cat.maj, "same",
NA))))))))))))
write.table(cmip6s, "cmip6s.txt")
cmip6s <- read.table("cmip6s.txt")
cat mean = category of aridity obtained by using the multimodel mean AI cat maj = category of aridity obtained by using the category majoritarily obtained in the 13 models
cmip6s %>% group_by(model, period) %>% summarise(maj.equal.mean = table(same.catAI)[2], maj.diff.mean = table(same.catAI)[1], prop.same = round(maj.equal.mean/n()*100,1)) %>% knitr::kable(col.names = c("Model","Period","Number of gridcells in which cat.maj = cat.mean", "Number of gridcells in which cat.maj /= cat.mean", "Proportion of cells with the same category (%)")) %>% kableExtra::kable_styling(bootstrap_options = "bordered")
| Model | Period | Number of gridcells in which cat.maj = cat.mean | Number of gridcells in which cat.maj /= cat.mean | Proportion of cells with the same category (%) |
|---|---|---|---|---|
| SSP245 | 2030_2060 | 19884 | 1798 | 89.3 |
| SSP245 | 2070_2100 | 19890 | 1792 | 89.3 |
| SSP370 | 2030_2060 | 19988 | 1694 | 89.8 |
| SSP370 | 2070_2100 | 19911 | 1771 | 89.4 |
| SSP585 | 2030_2060 | 19799 | 1883 | 88.9 |
| SSP585 | 2070_2100 | 19796 | 1886 | 88.9 |
| historical | 1850_1880 | 19907 | 1775 | 89.4 |
| historical | 1970_2000 | 19880 | 1802 | 89.3 |
| historical | 1985_2015 | 19936 | 1746 | 89.6 |
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
cmip6s$cat.maj <- factor(cmip6s$cat.maj, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
ggplot(subset(cmip6s, cat.AI != "Cold"))+
geom_col(aes(x= period, y = cat.AI, fill = cat.AI), just = -0.2, width = 0.3)+
geom_col(aes(x = period, y = cat.maj, fill = cat.maj), just = 1.2, width = 0.3)+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "", y ="")+
facet_grid(rows = vars(model), switch = "y")+
theme_minimal()+theme(axis.text.y = element_blank())
tab <- rbind(table(cmip6s$cat.AI)/length(cmip6s$cat.AI)*100, table(cmip6s$cat.maj)/length(cmip6s$cat.maj)*100) %>% t() %>% as.data.frame() %>% setNames(c("Cat mean", "Cat maj"))
knitr::kable(tab, digits =2, caption = "Proportion of gridcells in each aridity category") %>% kable_styling(bootstrap_options = "bordered")
| Cat mean | Cat maj | |
|---|---|---|
| Hyperarid | 3.51 | 4.18 |
| Arid | 6.25 | 7.43 |
| Semi-arid | 7.63 | 9.19 |
| Dry subhumid | 3.39 | 0.84 |
| Humid | 28.75 | 28.53 |
| Cold | 47.90 | 47.23 |
map_list <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.AI))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}
}
col1 <- ggarrange(plotlist = map_list, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.mean for gridcells in which\ncat.maj and cat.mean are different")
map_list2 <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}
}
col2 <- ggarrange(plotlist = map_list2, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.maj for gridcells in which\ncat.maj and cat.mean are different")
ggarrange(col1,col2,ncol = 2, common.legend = T)
ggpubr::ggarrange(plotlist = list(
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat)+
labs(title = "Category chosen in majority by models", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none"),
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat)+
labs(title = "Multimodel average categories of AI", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none")),
ncol = 2)
map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
map_list2 <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
# geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[i]] <- g
}
ggarrange(
annotate_figure(ggarrange(plotlist = map_list, nrow = 3, common.legend = T, legend = "bottom"), top = "Majority category among models"),
annotate_figure(ggarrange(plotlist = map_list2, nrow = 3, common.legend = T, legend = "bottom"), top = "Multimodel average category"),
ncol = 2, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == j & model == i & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI maj", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
map_list2 <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
# geom_point(data = subset(cmip6s, period == j & model == i & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI mean", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}}
ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
ggarrange(plotlist = map_list2, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid","Humid","Cold"))
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = ".", col = "grey20", size = 0.1)+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",i , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = ".", col = "grey20", size = 0.1)+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",index, sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
quantile(cmip6s$AI.mean, probs = seq(0,1,0.1), na.rm = T)
## 0% 10% 20% 30% 40% 50%
## 2.902197e-03 1.993698e-01 5.922059e-01 1.019621e+00 1.457766e+00 2.127218e+00
## 60% 70% 80% 90% 100%
## 3.578980e+00 1.168470e+01 2.438867e+01 4.856852e+01 5.329875e+06
ai.breaks <- c(0,0.03,0.2,0.5,0.65,1,10,20,30)
colscale <- colorvec[-c(5,6)]
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Aridity index")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Aridity index", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
v<-NULL
v2<-NULL
for (N in 5:30) {
v<-c(v,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N*2)))# proba d'avoir 2/3 de même signe à partir de N modèles, lorsque N change
v2<-c(v2,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N)))# proba d'avoir 2/3 de signe >0 à partir de N modèles, lorsque N change
}
plot(5:30,v,type="l",ylab="Proba(>2/3 models...)",xlab="Nb of models")
lines(5:30,v2,col="red")
ggpubr::ggarrange(plotlist = list(
ggplot(subset(cmip6s, diff.AI.mean < 1 & diff.AI.mean > -1 & model == "historical"))+
geom_boxplot(aes(x=period, y = - diff.AI.mean, col = period), outliers = F)+
scale_color_manual(values = c("#1A9850","#66BD63","#A6D96A"))+
labs(x = "", y = "Difference of AI between each period and\nthe reference period 1970-2000")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)),
ggplot(subset(cmip6s, diff.AI.mean < 1 & diff.AI.mean > -1 & model != "historical"))+
geom_boxplot(aes(x=period, y = diff.AI.mean, col = period), outliers = F)+
scale_color_manual(values = c("#FDAE61","#F46D43"))+
scale_y_continuous(position = "right")+
labs(y = "", x = "")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))),
widths = c(1.5,3),
ncol = 2, nrow = 1
)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = ", ")
g <- ggplot()+
geom_point(data = subset(cmip6s, model == i & period == j & plus.AI > 10), aes(x = lon, y = lat, col = "plus"), shape = "+")+
geom_point(data = subset(cmip6s, model == i & period == j & minus.AI > 10), aes(x = lon, y = lat, col = "minus"), shape = "-")+
scale_color_manual(values = colorvec[c(1,11)])+
labs(color = "", x = "", y = "", title = index)+
theme_minimal()
map_list[[index]] <- g
}
}
ggarrange(plotlist = map_list, ncol = 2, nrow = 3, common.legend = T)
map_list <- list()
for(i in c("1850_1880","1985_2015")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == i & model == "historical" & diff.AI.mean > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_tile(data = subset(cmip6s, period == i & model == "historical" & diff.AI.mean < 0), aes(x=lon, y = lat, fill = "dryer"))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", size = 0.5)+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(title = paste("AI changes between",i , "and 1970-2000", sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & diff.AI.mean > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_tile(data = subset(cmip6s, period == j & model == i & diff.AI.mean < 0), aes(x=lon, y = lat, fill = "dryer"))+
geom_point(data = subset(cmip6s, period == j & model == i & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(fill = "Compared to 1970-2000", title = i)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
ano.quantiles <- cmip6s %>% group_by(model, period) %>% summarise(q10 = quantile(diff.AI.mean, probs = 0.1, na.rm =T),
q25 = quantile(diff.AI.mean, probs = 0.25, na.rm =T),
q50 = quantile(diff.AI.mean, probs = 0.5, na.rm =T),
q75 = quantile(diff.AI.mean, probs = 0.75, na.rm =T),
q90 = quantile(diff.AI.mean, probs = 0.9, na.rm =T))
ano.quantiles <- cmip6s %>% summarise(q10 = quantile(diff.AI.mean, probs = 0.1, na.rm =T),
q25 = quantile(diff.AI.mean, probs = 0.25, na.rm =T),
q50 = quantile(diff.AI.mean, probs = 0.5, na.rm =T),
q75 = quantile(diff.AI.mean, probs = 0.75, na.rm =T),
q90 = quantile(diff.AI.mean, probs = 0.9, na.rm =T))
qbreaks <- c(-5, -1,-0.1, -0.01, 0, 0.01, 0.1, 1,5)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")
“diff.AI.mean” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
AS OF NOW THE SAME QUANTILES ARE USED FOR ALL MAPS.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.AI.mean))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.AI.mean))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Compared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
cmip6s$diff.AI.mean.percent <- with(cmip6s, (AI.mean-AI.ref.mean)/AI.ref.mean*100)
pbreaks <- c(-40, -30,-20, -10, 0, 10, 20, 30, 40)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef","white","white", "#fddbc7", "#f4a582", "#d6604d", "#b2182b")
“diff.AI.mean” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = pbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.AI.mean.percent))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 2, common.legend = T, legend = "bottom") %>% annotate_figure(top = "% change of AI index compared to 1970-2000")
#,guide = guide_legend(label.theme = element_text(angle = 0))
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = pbreaks
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.AI.mean.percent))+
borders(colour = "grey60", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale))+
labs(fill = "% change of AI index\ncompared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
cmip6s$change.catmaj <- with(cmip6s, ifelse(cat.maj == cat.maj.ref, 0, 1))
cmip6s$change.cat.maj <- with(cmip6s, paste(cat.maj.ref, cat.maj, sep = " to "))
cmip6s$change.cat.maj <- with(cmip6s, ifelse(cat.maj.ref == "Arid" & cat.maj %in% c("Cold","Humid"),"Arid to cold or humid",
ifelse(cat.maj.ref == "Arid" & cat.maj %in% c("Semi-Arid","Dry subhumid"), "Arid to semi-arid or dry subhumid",
ifelse(cat.maj.ref == "Arid" & cat.maj == "Hyperarid", "Arid to hyperarid",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj == "Dry subhumid", "Semi-arid to dry subhumid",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj == "Arid", "Semi-arid to arid",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj == "Hyperarid", "Semi-arid to hyperarid",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj %in% c("Humid","Cold"), "Semi-arid to humid or cold",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Semi-arid","Arid","Hyperarid"), "Dry subhumid to arid, semi-arid or hyperarid",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Humid","Cold"), "Dry subhumid to humid or cold",
ifelse(cat.maj.ref == "Humid" & cat.maj == "Cold","Humid to cold",
ifelse(cat.maj.ref == "Humid" & cat.maj %in% c("Dry subhumid", "Semi-arid"), "Humid to dry-subhumid or semi-arid",
ifelse(cat.maj.ref == "Humid" & cat.maj %in% c("Arid", "Hyperarid"), "Humid to arid or hyperarid",
ifelse(cat.maj.ref == "Cold" & cat.maj == "Humid", "Cold to humid",
ifelse(cat.maj.ref == "Cold" & cat.maj %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "Cold to dryland",
ifelse(cat.maj.ref == "Hyperarid" & cat.maj == "Arid", "Hyperarid to arid",
ifelse(cat.maj.ref == "Hyperarid" & cat.maj %in% c("Semi-arid","Dry-subhumid"),"Hyperarid to semi arid or dry subhumid",
ifelse(cat.maj.ref == "Hyperarid" & cat.maj %in% c("Humid", "Cold"), "Hyperarid to humid or cold", NA))))))))))))))))))
cmip6s$change.cat.maj <- factor(cmip6s$change.cat.maj,
levels = c("Arid to hyperarid","Semi-arid to arid", "Dry subhumid to arid, semi-arid or hyperarid", "Humid to dry-subhumid or semi-arid", "Cold to humid","Hyperarid to arid", "Arid to semi-arid or dry subhumid", "Dry subhumid to humid or cold", "Semi-arid to dry subhumid", "Semi-arid to humid or cold"))
table(cmip6s$change.cat.maj)
##
## Arid to hyperarid
## 0
## Semi-arid to arid
## 0
## Dry subhumid to arid, semi-arid or hyperarid
## 0
## Humid to dry-subhumid or semi-arid
## 0
## Cold to humid
## 0
## Hyperarid to arid
## 0
## Arid to semi-arid or dry subhumid
## 0
## Dry subhumid to humid or cold
## 0
## Semi-arid to dry subhumid
## 0
## Semi-arid to humid or cold
## 0
colors_changes <- c("Hyperarid to arid" = "#E7D7B6",
"Arid to semi-arid or dry subhumid" = colorvec[7],
"Dry subhumid to humid or cold" = colorvec[8],
"Semi-arid to dry subhumid" = colorvec[9],
"Semi-arid to humid or cold" = colorvec[10],
"Humid to cold" = colorvec[11],
"Cold to humid" = "#E7D7B6",
"Humid to dry-subhumid or semi-arid" = colorvec[5],
"Dry subhumid to arid, semi-arid or hyperarid" = colorvec[4],
"Semi-arid to arid" = colorvec[3],
"Arid to hyperarid" = colorvec[1])
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.maj.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & cat.maj.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
# geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & nb.maj > 10), aes(x= lon, y = lat, fill = "'"))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
# scale_fill_viridis_d()+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.maj.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+
ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & cat.maj.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
cmip6s$change.catmean <- with(cmip6s, ifelse(cat.AI == cat.AI.ref, 0, 1))
cmip6s$change.cat.mean <- with(cmip6s, paste(cat.AI.ref, cat.AI, sep = " to "))
unique(cmip6s$change.cat)
## NULL
# cmip6s$change.cat.mean <- with(cmip6s,
# ifelse(cat.AI.ref == "Arid" & cat.AI %in% c("Cold","Humid"),"Arid to cold or humid",
# ifelse(cat.AI.ref == "Arid" & cat.AI %in% c("Semi-Arid","Dry subhumid"), "Arid to semi-arid or dry subhumid",
# ifelse(cat.AI.ref == "Arid" & cat.AI == "Hyperarid", "Arid to hyperarid",
# ifelse(cat.AI.ref == "Semi-arid" & cat.AI == "Dry subhumid", "Semi-arid to dry subhumid",
# ifelse(cat.AI.ref == "Semi-arid" & cat.AI == "Arid", "Semi-arid to arid",
# ifelse(cat.AI.ref == "Semi-arid" & cat.AI == "Hyperarid", "Semi-arid to hyperarid",
# ifelse(cat.AI.ref == "Semi-arid" & cat.AI %in% c("Humid","Cold"), "Semi-arid to humid or cold",
# ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Semi-arid","Arid","Hyperarid"), "Dry subhumid to arid, semi-arid or hyperarid",
# ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Humid","Cold"), "Dry subhumid to humid or cold",
# ifelse(cat.AI.ref == "Humid" & cat.AI == "Cold","Humid to cold",
# ifelse(cat.AI.ref == "Humid" & cat.AI %in% c("Dry subhumid","Semi-arid"), "Humid to dry-subhumid or semi-arid",
# ifelse(cat.AI.ref == "Humid" & cat.AI %in% c("Arid", "Hyperarid"), "Humid to arid or hyperarid",
# ifelse(cat.AI.ref == "Cold" & cat.AI == "Humid", "Cold to humid",
# ifelse(cat.AI.ref == "Cold" & cat.AI %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "Cold to dryland",
# ifelse(cat.AI.ref == "Hyperarid" & cat.AI == "Arid", "Hyperarid to arid",
# ifelse(cat.AI.ref == "Hyperarid" & cat.AI %in% c("Semi-arid","Dry-subhumid"),"Hyperarid to semi arid or dry subhumid",
# ifelse(cat.AI.ref == "Hyperarid" & cat.AI %in% c("Humid", "Cold"), "Hyperarid to humid or cold",
# NA))))))))))))))))))
table(cmip6s$change.cat.mean)
##
## Arid to Arid Arid to Hyperarid
## 11080 639
## Arid to Semi-arid Cold to Cold
## 340 95966
## Dry subhumid to Dry subhumid Dry subhumid to Humid
## 4022 288
## Dry subhumid to Semi-arid Humid to Dry subhumid
## 2242 2484
## Humid to Humid Humid to Semi-arid
## 57318 198
## Hyperarid to Arid Hyperarid to Hyperarid
## 240 6402
## NA to NA Semi-arid to Arid
## 5130 1209
## Semi-arid to Dry subhumid Semi-arid to Semi-arid
## 277 12514
cmip6s$change.cat.mean <- factor(cmip6s$change.cat.mean,
levels = c("Arid to Hyperarid","Semi-arid to Arid", "Dry subhumid to Semi-arid", "Humid to Semi-arid", "Humid to Dry subhumid", "Cold to Humid","Hyperarid to arid", "Arid to Semi-arid", "Dry subhumid to Humid", "Semi-arid to Dry subhumid", "Semi-arid to Humid"))
colors_changes <- c("Hyperarid to Arid" = "#E7D7B6",
"Arid to Semi-arid" = colorvec[7],
"Dry subhumid to Humid" = colorvec[8],
"Semi-arid to Dry subhumid" = colorvec[9],
"Semi-arid to Humid" = colorvec[10],
"Humid to Cold" = colorvec[11],
"Cold to Humid" = "#E7D7B6",
"Humid to Dry subhumid" = colorvec[5],
"Humid to Semi-arid" = colorvec[4],
"Dry subhumid to Semi-arid" = colorvec[3],
"Semi-arid to Arid" = colorvec[2],
"Arid to Hyperarid" = colorvec[1])
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmean == 1 & cat.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & cat.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list, ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_tile(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & cat.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == "2070_2100" & model == "SSP585" & change.catmean == 1), aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of aridity category\nfor the period 2070-2100\nand SSP 5-8.5,\ncompared to 1970-2000", title = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3))
print(g)
tab <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.AI)[1], Hyperarid = table(cat.AI)[2], Arid = table(cat.AI)[3], Semiarid = table(cat.AI)[4], Drysubhumid = table(cat.AI)[5], Humid = table(cat.AI)[6], total = n())
write.table(tab, "tab.catAI.txt")
tab.future <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future[order(tab.future$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T)
| model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.4 | 6.0 | 7.1 | 3.3 | 19.8 | 28.8 | 48.9 | 2.6 |
| 2 | historical | 1970_2000 | 3.3 | 6.0 | 7.0 | 3.3 | 19.6 | 27.8 | 49.9 | 2.6 |
| 3 | historical | 1985_2015 | 3.3 | 6.1 | 6.9 | 3.3 | 19.6 | 27.3 | 50.6 | 2.6 |
| 4 | SSP245 | 2030_2060 | 3.5 | 6.3 | 7.8 | 3.3 | 20.9 | 28.8 | 47.7 | 2.6 |
| 7 | SSP245 | 2070_2100 | 3.6 | 6.4 | 7.8 | 3.5 | 21.3 | 29.4 | 46.6 | 2.6 |
| 5 | SSP370 | 2030_2060 | 3.2 | 6.7 | 7.6 | 3.0 | 20.5 | 28.8 | 48.1 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.0 | 6.3 | 8.3 | 3.7 | 22.3 | 28.5 | 46.6 | 2.6 |
| 6 | SSP585 | 2030_2060 | 3.6 | 6.2 | 7.9 | 3.5 | 21.2 | 29.3 | 47.0 | 2.6 |
| 9 | SSP585 | 2070_2100 | 3.8 | 6.4 | 8.4 | 3.6 | 22.2 | 30.1 | 45.1 | 2.6 |
# ggplot(tab.future, aes(x = period))+
# geom_point(aes(y = Hyperarid, col = "Hyperarid", shape = model))+
# geom_line(aes(y=Hyperarid, col = "Hyperarid", group = model, lty = model))+
# geom_point(aes(y = Arid, col = "Arid", shape = model))+
# geom_line(aes(y=Arid, col = "Arid", group = model, lty = model))+
# geom_point(aes(y = get('Semi-arid'), col = "Semi-arid", shape = model))+
# geom_line(aes(y=get('Semi-arid'), col = "Semi-arid", group = model, lty = model))+
# geom_point(aes(y = get('Dry subhumid'), col = "Dry subhumid", shape = model))+
# geom_line(aes(y=get('Dry subhumid'), col = "Dry subhumid", group = model, lty = model))+
# geom_point(aes(y = Humid, col = "Humid", shape = model))+
# geom_line(aes(y= Humid, col = "Humid", group = model, lty = model))+
# scale_color_manual(values = col.cat)+
# theme_minimal()
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent, "tab.percent.txt")
tab.percent <- read.table("tab.percent.txt")
df245 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP245")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df245$cat.AI <- factor(df245$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df370 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP370")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df370$cat.AI <- factor(df370$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df585 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.percent, model == "SSP585")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df585$cat.AI <- factor(df585$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
tab.maj <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.maj)[1], Hyperarid = table(cat.maj)[2], Arid = table(cat.maj)[3], Semiarid = table(cat.maj)[4], Drysubhumid = table(cat.maj)[5], Humid = table(cat.maj)[6], total = n())
write.table(tab.maj, "tab.catmaj.txt")
tab.future.maj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.maj) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future.maj[order(tab.future.maj$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T)
| model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 4.2 | 7.1 | 8.7 | 0.8 | 20.8 | 28.3 | 48.3 | 2.6 |
| 2 | historical | 1970_2000 | 4.1 | 7.1 | 8.6 | 0.7 | 20.5 | 28.1 | 48.9 | 2.6 |
| 3 | historical | 1985_2015 | 4.0 | 7.0 | 8.4 | 0.9 | 20.3 | 27.3 | 49.9 | 2.6 |
| 4 | SSP245 | 2030_2060 | 4.0 | 7.8 | 9.3 | 0.8 | 21.9 | 28.4 | 47.2 | 2.6 |
| 7 | SSP245 | 2070_2100 | 4.2 | 7.7 | 9.4 | 0.9 | 22.2 | 29.1 | 46.1 | 2.6 |
| 5 | SSP370 | 2030_2060 | 4.1 | 7.2 | 9.4 | 0.8 | 21.5 | 28.6 | 47.3 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.5 | 7.7 | 10.0 | 0.9 | 23.1 | 28.4 | 45.9 | 2.6 |
| 6 | SSP585 | 2030_2060 | 4.2 | 7.5 | 9.6 | 1.0 | 22.3 | 28.8 | 46.3 | 2.6 |
| 9 | SSP585 | 2070_2100 | 4.5 | 7.9 | 9.4 | 0.9 | 22.7 | 30.0 | 44.6 | 2.6 |
tab.flow.catmaj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
df245 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP245")) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow.catmaj, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent.cont, "tab.percent.continent.txt")
tab.percent.cont <- read.table("tab.percent.continent.txt")
tab_list_percent <- list()
tab_list_sd <- list()
for(i in unique(tab.percent.cont$Continent)){
tab.i <- subset(tab.percent.cont, Continent == i)
df.percent <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "percent")
df.sd <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "sd.percent")
# sd for a sum: square root of sum of squared sd
drycats <- which(names(df.percent) %in% c("Hyperarid","Arid","Semi-arid","Dry subhumid"))
df.percent <- df.percent %>% mutate("Sum drylands" = rowSums(.[drycats], na.rm = T))
df.sd <- df.sd %>% mutate("Sum drylands" = round(sqrt(rowSums((.[drycats])^2, na.rm = T)),2))
missing <- setdiff(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"), names(df.percent))
df.percent[, missing] <- 0
df.sd[,missing] <- 0
df.percent <- df.percent %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.percent[is.na(df.percent)] <- 0
tab_list_percent[[i]] <- df.percent
df.sd <- df.sd %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.sd[is.na(df.sd)] <- 0
tab_list_sd[[i]] <- df.sd
}
k_list <- list()
kdiff_list <- list()
for(i in names(tab_list_percent)){
df <- tab_list_percent[[i]] %>% select(c("model", "period"))
prop.ref <- tab_list_percent[[i]] %>% filter(period == "1970_2000")
prop.ref <- prop.ref %>% rbind(prop.ref[rep(1,dim(df)[1]-1),])
df2 <- tab_list_percent[[i]][,c(3:10)] - prop.ref[,c(3:10)] %>% round(1)
diff.prop.percent <- cbind(df, df2)
sd.ref <- tab_list_sd[[i]] %>% filter(period == "1970_2000")
sd.ref <- sd.ref %>% rbind(sd.ref[rep(1,dim(df)[1]-1),])
df3 <- sqrt(tab_list_sd[[i]][,c(3:10)]^2 + sd.ref[,c(3:10)]^2) %>% round(1)
diff.sd.percent <- cbind(df, df3)
df$Hyperarid <- paste(tab_list_percent[[i]]$Hyperarid, tab_list_sd[[i]]$Hyperarid, sep = " ± ")
df$Arid <- paste(tab_list_percent[[i]]$Arid, tab_list_sd[[i]]$Arid, sep = " ± ")
df$'Semi-Arid' <- paste(tab_list_percent[[i]][,5], tab_list_sd[[i]][,5], sep = " ± ")
df$'Dry subhumid' <- paste(tab_list_percent[[i]][,6], tab_list_sd[[i]][,6], sep = " ± ")
df$'Sum drylands' <- paste(tab_list_percent[[i]][,7], tab_list_sd[[i]][,7], sep = " ± ")
df$Humid <- paste(tab_list_percent[[i]]$Humid, tab_list_sd[[i]]$Humid, sep = " ± ")
df$Cold <- paste(tab_list_percent[[i]]$Cold, tab_list_sd[[i]]$Cold, sep = " ± ")
df.diff <- df
df.diff$Hyperarid <- paste(round(diff.prop.percent$Hyperarid, 1), round(diff.sd.percent$Hyperarid, 1), sep = " ± ")
df.diff$Arid <- paste(round(diff.prop.percent$Arid, 1), round(diff.sd.percent$Arid, 1), sep = " ± ")
df.diff$'Semi-Arid' <- paste(round(diff.prop.percent[,5], 1), round(diff.sd.percent[,5], 1), sep = " ± ")
df.diff$'Dry subhumid' <- paste(round(diff.prop.percent[,6], 1), round(diff.sd.percent[,6], 1), sep = " ± ")
df.diff$'Sum drylands' <- paste(round(diff.prop.percent[,7],1), round(diff.sd.percent[,7], 1), sep = " ± ")
df.diff$Humid <- paste(round(diff.prop.percent$Humid, 1), round(diff.sd.percent$Humid, 1), sep = " ± ")
df.diff$Cold <- paste(round(diff.prop.percent$Cold, 1), round(diff.sd.percent$Cold, 1), sep = " ± ")
k_list[[i]] <- kable(df[order(df$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = colorvec[5]) %>% row_spec(2, bold = T)
kdiff_list[[i]] <- kable(df.diff[order(df.diff$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = colorvec[5]) %>% row_spec(2, bold = T)
}
k_list
$AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 27.2 ± 4.7 | 17.1 ± 4.9 | 15.6 ± 3.6 | 6.3 ± 1.4 | 66.2 ± 7.81 | 33.7 ± 7.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 26.9 ± 4.7 | 16.7 ± 4.2 | 15.1 ± 3.4 | 6.2 ± 1.5 | 64.9 ± 7.32 | 32.8 ± 6.2 | 2.3 ± 0 |
| 3 | historical | 1985_2015 | 26.5 ± 4.8 | 17.1 ± 4.6 | 15.4 ± 3.3 | 6 ± 1.5 | 65 ± 7.57 | 32.7 ± 6.3 | 2.2 ± 0 |
| 4 | SSP245 | 2030_2060 | 27 ± 4.8 | 18 ± 4.9 | 16.5 ± 3.5 | 6.4 ± 1.7 | 67.9 ± 7.89 | 32 ± 7.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 27.9 ± 4.7 | 17.4 ± 5 | 17.2 ± 3.4 | 6.2 ± 1.5 | 68.7 ± 7.8 | 31.3 ± 7.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 26 ± 5 | 16.6 ± 3.6 | 17.2 ± 1.8 | 6.8 ± 1 | 66.6 ± 6.5 | 32.3 ± 6.7 | 1.1 ± 0 |
| 8 | SSP370 | 2070_2100 | 28 ± 3.5 | 17.6 ± 4.8 | 16.6 ± 3.1 | 6.2 ± 1.6 | 68.4 ± 6.89 | 30.4 ± 6.4 | 1.2 ± 0 |
| 6 | SSP585 | 2030_2060 | 27.1 ± 4.6 | 17.6 ± 4.8 | 16.8 ± 3.5 | 6.3 ± 1.5 | 67.8 ± 7.66 | 32.1 ± 7.5 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 29.7 ± 10.5 | 17.3 ± 5.3 | 16.2 ± 4.2 | 6 ± 1.5 | 69.2 ± 12.58 | 30.9 ± 8.6 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.9 ± 2.3 | 12.2 ± 1.9 | 9.2 ± 1.5 | 4 ± 1.4 | 29.3 ± 3.62 | 32.4 ± 7.7 | 38 ± 9.2 |
| 2 | historical | 1970_2000 | 3.8 ± 2.1 | 12 ± 1.8 | 8.9 ± 1.5 | 3.8 ± 1.1 | 28.5 ± 3.33 | 30.7 ± 8.5 | 40.5 ± 9.6 |
| 3 | historical | 1985_2015 | 3.7 ± 2.2 | 11.8 ± 1.6 | 8.6 ± 1.5 | 3.7 ± 1.1 | 27.8 ± 3.3 | 30.1 ± 7.6 | 41.8 ± 8.8 |
| 4 | SSP245 | 2030_2060 | 3.5 ± 1.8 | 12.5 ± 1.6 | 9.4 ± 1.6 | 4.3 ± 1.7 | 29.7 ± 3.35 | 34.3 ± 8.1 | 35.8 ± 9.2 |
| 7 | SSP245 | 2070_2100 | 3.7 ± 2 | 12.5 ± 1.7 | 9.5 ± 1.7 | 4.2 ± 1.7 | 29.9 ± 3.56 | 36.2 ± 8.6 | 33.7 ± 9.3 |
| 5 | SSP370 | 2030_2060 | 3.4 ± 2.3 | 12.4 ± 1.5 | 8.9 ± 1.7 | 3.7 ± 1.3 | 28.4 ± 3.48 | 34 ± 7.2 | 37.3 ± 9.5 |
| 8 | SSP370 | 2070_2100 | 4.6 ± 1.9 | 12.5 ± 1.5 | 9.6 ± 1.5 | 3.8 ± 1.2 | 30.5 ± 3.09 | 35.2 ± 9.2 | 34.1 ± 9.7 |
| 6 | SSP585 | 2030_2060 | 3.8 ± 2 | 12.5 ± 1.6 | 9.5 ± 1.8 | 4.2 ± 1.6 | 30 ± 3.52 | 35.4 ± 8.4 | 34.4 ± 9.3 |
| 9 | SSP585 | 2070_2100 | 4.8 ± 3.2 | 13.1 ± 3.2 | 10.4 ± 3.4 | 4.2 ± 1.5 | 32.5 ± 5.86 | 36.5 ± 11.7 | 30.7 ± 9.9 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 8.4 ± 3.6 | 30.1 ± 5.1 | 16.1 ± 8.7 | 54.6 ± 10.71 | 45 ± 6.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 8.4 ± 4.1 | 29.7 ± 7.6 | 14.7 ± 7.8 | 52.8 ± 11.64 | 44.9 ± 12 | 2 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 9 ± 4.2 | 30.7 ± 7 | 14.8 ± 7.4 | 54.5 ± 11.02 | 43.8 ± 11.5 | 1.4 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 11.9 ± 6.2 | 36.3 ± 8 | 13.9 ± 6.2 | 62.1 ± 11.87 | 37.6 ± 8.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 12.3 ± 5.5 | 38.1 ± 8.5 | 14.4 ± 7.9 | 64.8 ± 12.84 | 34.8 ± 8.6 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 12.6 ± 6.3 | 31.4 ± 7.1 | 15 ± 7.9 | 59 ± 12.35 | 40.7 ± 11.8 | 0 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 16 ± 5.5 | 35.5 ± 9.5 | 13.2 ± 5.2 | 68.7 ± 13.21 | 30.9 ± 10.6 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 13 ± 7 | 37.6 ± 9 | 14.1 ± 7.1 | 64.7 ± 13.43 | 35 ± 9.4 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 16.6 ± 8 | 41.2 ± 7.7 | 13.3 ± 7.7 | 73.1 ± 13.72 | 26.6 ± 8.8 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 1.9 ± 1.2 | 9.6 ± 4.1 | 4.5 ± 2.8 | 16 ± 5.11 | 49 ± 15.3 | 34.8 ± 20.2 |
| 2 | historical | 1970_2000 | 0 ± 0 | 1.5 ± 1 | 8.9 ± 3.4 | 3.9 ± 2.1 | 14.3 ± 4.12 | 44.9 ± 16.1 | 40.6 ± 20.4 |
| 3 | historical | 1985_2015 | 0 ± 0 | 1.3 ± 1 | 8.4 ± 2.9 | 3.8 ± 2 | 13.5 ± 3.66 | 46 ± 16.1 | 40.3 ± 19.9 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 1.9 ± 0.9 | 10.3 ± 3.6 | 5.1 ± 1.9 | 17.3 ± 4.17 | 52.7 ± 15.4 | 29.8 ± 19.7 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 2.2 ± 1.1 | 10.1 ± 4.8 | 5.1 ± 2.3 | 17.4 ± 5.44 | 55.4 ± 14.4 | 27 ± 18.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 2.1 ± 1 | 9.7 ± 4.7 | 4.8 ± 2.5 | 16.6 ± 5.42 | 52.7 ± 15.7 | 30.4 ± 20 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 3.1 ± 1.1 | 11.4 ± 5.9 | 6.4 ± 3.8 | 20.9 ± 7.1 | 52.8 ± 14.3 | 26 ± 18.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 2.3 ± 1.1 | 10.5 ± 3.7 | 4.9 ± 2.4 | 17.7 ± 4.55 | 55.2 ± 15.8 | 26.9 ± 19.9 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 3 ± 1.7 | 11.1 ± 5.4 | 6.1 ± 2.9 | 20.2 ± 6.36 | 57.2 ± 12.8 | 22.5 ± 17.3 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.9 ± 4.9 | 23.6 ± 7 | 21.3 ± 8.8 | 8.8 ± 3.3 | 59.6 ± 12.7 | 24.9 ± 14.2 | 14.5 ± 0 |
| 2 | historical | 1970_2000 | 7 ± 5.5 | 26.5 ± 7.5 | 23.3 ± 10.3 | 9.9 ± 3.2 | 66.7 ± 14.24 | 27.5 ± 16.5 | 4.5 ± 7.5 |
| 3 | historical | 1985_2015 | 7.1 ± 5.9 | 26.3 ± 7.1 | 22.2 ± 7.7 | 10.3 ± 4.2 | 65.9 ± 12.73 | 27.8 ± 14.3 | 5.2 ± 7 |
| 4 | SSP245 | 2030_2060 | 9.9 ± 6.6 | 28.9 ± 8.6 | 26.7 ± 8.9 | 9.4 ± 3.4 | 74.9 ± 14.43 | 23.1 ± 16.2 | 0.9 ± 0 |
| 7 | SSP245 | 2070_2100 | 11.7 ± 5.7 | 28.6 ± 8.8 | 27.2 ± 8.1 | 9.5 ± 3.1 | 77 ± 13.61 | 21.9 ± 13.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.3 ± 6.3 | 28.2 ± 8.4 | 26 ± 8.7 | 9 ± 2.8 | 73.5 ± 13.92 | 24.8 ± 14 | 0.5 ± 0 |
| 8 | SSP370 | 2070_2100 | 13.2 ± 6.2 | 27.3 ± 8.6 | 28.3 ± 9.7 | 8.7 ± 3.6 | 77.5 ± 14.81 | 21.2 ± 16.2 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 11.1 ± 6.4 | 28.8 ± 8.3 | 27.5 ± 8.8 | 8.7 ± 3 | 76.1 ± 14.01 | 22.6 ± 14.1 | 0.2 ± 0 |
| 9 | SSP585 | 2070_2100 | 15.2 ± 5.7 | 29.1 ± 10.1 | 27.9 ± 8.8 | 8 ± 2.8 | 80.2 ± 14.82 | 18.6 ± 13.6 | 0 ± 0 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 0.2 | 5.8 ± 5.6 | 4.3 ± 2.6 | 10.5 ± 6.18 | 35.1 ± 9 | 54.2 ± 10.9 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0.4 ± 0.2 | 4.3 ± 4.4 | 4.2 ± 2.4 | 8.9 ± 5.02 | 34.5 ± 9.8 | 56.5 ± 11.7 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.3 ± 0.2 | 4.6 ± 3.8 | 4.1 ± 2.2 | 9 ± 4.4 | 33.6 ± 10.3 | 57.2 ± 11.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 0.3 | 6.5 ± 5.5 | 4.7 ± 2.6 | 11.6 ± 6.09 | 37.5 ± 8.7 | 50.8 ± 11.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.5 ± 0.3 | 6.4 ± 5.1 | 5.1 ± 2.9 | 12 ± 5.87 | 39.7 ± 9 | 48.1 ± 11 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 5.9 ± 4.8 | 4.6 ± 2.7 | 11 ± 5.52 | 37 ± 9.7 | 51.8 ± 12.2 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.8 ± 0.7 | 7.6 ± 4 | 5.2 ± 2 | 13.7 ± 4.53 | 38.4 ± 10.6 | 47.7 ± 11.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 6.6 ± 5.2 | 5.2 ± 2.8 | 12.3 ± 5.92 | 38.2 ± 9.1 | 49.3 ± 11.7 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 1 ± 1.4 | 7 ± 6.1 | 5.2 ± 2.5 | 13.4 ± 6.74 | 42.1 ± 9.3 | 44.5 ± 11.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.3 ± 0 | 32 ± 26 | 36.7 ± 17.1 | 6.6 ± 3.1 | 80.6 ± 31.27 | 18.9 ± 12.5 | 0.3 ± 0.2 |
| 2 | historical | 1970_2000 | 5.2 ± 0 | 31.3 ± 26.4 | 36.8 ± 17.4 | 6.8 ± 3.1 | 80.1 ± 31.77 | 19.3 ± 12.8 | 0.5 ± 0.4 |
| 3 | historical | 1985_2015 | 5.2 ± 0 | 33.4 ± 26.1 | 35.5 ± 17.9 | 6.8 ± 3.2 | 80.9 ± 31.81 | 18.4 ± 12.3 | 0.6 ± 0.3 |
| 4 | SSP245 | 2030_2060 | 10.4 ± 0 | 35.8 ± 23.5 | 31.7 ± 15.1 | 5.7 ± 2.6 | 83.6 ± 28.05 | 15.9 ± 11.9 | 0.3 ± 0.3 |
| 7 | SSP245 | 2070_2100 | 10.5 ± 0 | 37.9 ± 22.2 | 29.1 ± 13.1 | 6.2 ± 3.3 | 83.7 ± 25.99 | 16.2 ± 12 | 0.1 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.7 ± 0 | 34.8 ± 22.6 | 33.4 ± 15.3 | 5.8 ± 2.5 | 84.7 ± 27.41 | 14.9 ± 8.1 | 0.3 ± 0.2 |
| 8 | SSP370 | 2070_2100 | 10.5 ± 10.2 | 38.1 ± 19.7 | 28.5 ± 13.8 | 5.7 ± 3.2 | 82.8 ± 26.32 | 16.8 ± 16.3 | 0.3 ± 0.3 |
| 6 | SSP585 | 2030_2060 | 5.6 ± 3.6 | 35.6 ± 25.3 | 33.8 ± 16.7 | 6.7 ± 3.6 | 81.7 ± 30.74 | 17.7 ± 13.8 | 0.3 ± 0.3 |
| 9 | SSP585 | 2070_2100 | 9.3 ± 6.4 | 39.4 ± 21.7 | 29.4 ± 12.9 | 5.5 ± 2.5 | 83.6 ± 26.16 | 16.2 ± 12.5 | 0 ± 0 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0.1 | 2.1 ± 1.7 | 15.2 ± 8.7 | 7.4 ± 2.6 | 24.8 ± 9.24 | 73.2 ± 11.7 | 1.9 ± 1 |
| 2 | historical | 1970_2000 | 0.1 ± 0.1 | 1.8 ± 1.3 | 13.9 ± 4.3 | 7.3 ± 2.3 | 23.1 ± 5.05 | 74.5 ± 7.3 | 2.3 ± 2.6 |
| 3 | historical | 1985_2015 | 0.2 ± 0.1 | 2 ± 1.2 | 14.5 ± 5.1 | 7.5 ± 2.4 | 24.2 ± 5.76 | 73.6 ± 7.7 | 2.1 ± 2.6 |
| 4 | SSP245 | 2030_2060 | 0.2 ± 0.2 | 2.7 ± 2.2 | 17.4 ± 9.6 | 8.8 ± 3.6 | 29.1 ± 10.49 | 69.4 ± 13.1 | 1.3 ± 0.9 |
| 7 | SSP245 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 3.5 | 18.3 ± 9.1 | 9.3 ± 4.6 | 31.1 ± 10.78 | 67.5 ± 14.7 | 1.3 ± 0.7 |
| 5 | SSP370 | 2030_2060 | 0.2 ± 0.1 | 2.2 ± 1.7 | 16.3 ± 5.5 | 8.8 ± 3.7 | 27.5 ± 6.84 | 70.5 ± 8.9 | 1.9 ± 1.6 |
| 8 | SSP370 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 2.5 | 18.6 ± 7 | 8.7 ± 3.8 | 30.8 ± 8.35 | 67.3 ± 10.8 | 1.8 ± 1.8 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 0.1 | 3.1 ± 2.5 | 18.6 ± 9 | 9.2 ± 4.4 | 31.1 ± 10.33 | 67.6 ± 13.1 | 1.2 ± 0.8 |
| 9 | SSP585 | 2070_2100 | 0.6 ± 1 | 4.4 ± 3.8 | 19.6 ± 9.5 | 9.7 ± 3.9 | 34.3 ± 11 | 64.4 ± 12.6 | 1.1 ± 0.7 |
kdiff_list
$AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.3 ± 6.6 | 0.4 ± 6.5 | 0.5 ± 5 | 0.1 ± 2.1 | 1.3 ± 10.7 | 0.9 ± 9.4 | -2.3 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 6.6 | 0 ± 5.9 | 0 ± 4.8 | 0 ± 2.1 | 0 ± 10.4 | 0 ± 8.8 | 0 ± 0 |
| 3 | historical | 1985_2015 | -0.4 ± 6.7 | 0.4 ± 6.2 | 0.3 ± 4.7 | -0.2 ± 2.1 | 0.1 ± 10.5 | -0.1 ± 8.8 | -0.1 ± 0 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 6.7 | 1.3 ± 6.5 | 1.4 ± 4.9 | 0.2 ± 2.3 | 3 ± 10.8 | -0.8 ± 9.8 | -2.3 ± 0 |
| 7 | SSP245 | 2070_2100 | 1 ± 6.6 | 0.7 ± 6.5 | 2.1 ± 4.8 | 0 ± 2.1 | 3.8 ± 10.7 | -1.5 ± 10 | -2.3 ± 0 |
| 5 | SSP370 | 2030_2060 | -0.9 ± 6.9 | -0.1 ± 5.5 | 2.1 ± 3.8 | 0.6 ± 1.8 | 1.7 ± 9.8 | -0.5 ± 9.1 | -1.2 ± 0 |
| 8 | SSP370 | 2070_2100 | 1.1 ± 5.9 | 0.9 ± 6.4 | 1.5 ± 4.6 | 0 ± 2.2 | 3.5 ± 10.1 | -2.4 ± 8.9 | -1.1 ± 0 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 6.6 | 0.9 ± 6.4 | 1.7 ± 4.9 | 0.1 ± 2.1 | 2.9 ± 10.6 | -0.7 ± 9.7 | -2.3 ± 0 |
| 9 | SSP585 | 2070_2100 | 2.8 ± 11.5 | 0.6 ± 6.8 | 1.1 ± 5.4 | -0.2 ± 2.1 | 4.3 ± 14.6 | -1.9 ± 10.6 | -2.3 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 3.1 | 0.2 ± 2.6 | 0.3 ± 2.1 | 0.2 ± 1.8 | 0.8 ± 4.9 | 1.7 ± 11.5 | -2.5 ± 13.3 |
| 2 | historical | 1970_2000 | 0 ± 3 | 0 ± 2.5 | 0 ± 2.1 | 0 ± 1.6 | 0 ± 4.7 | 0 ± 12 | 0 ± 13.6 |
| 3 | historical | 1985_2015 | -0.1 ± 3 | -0.2 ± 2.4 | -0.3 ± 2.1 | -0.1 ± 1.6 | -0.7 ± 4.7 | -0.6 ± 11.4 | 1.3 ± 13 |
| 4 | SSP245 | 2030_2060 | -0.3 ± 2.8 | 0.5 ± 2.4 | 0.5 ± 2.2 | 0.5 ± 2 | 1.2 ± 4.7 | 3.6 ± 11.7 | -4.7 ± 13.3 |
| 7 | SSP245 | 2070_2100 | -0.1 ± 2.9 | 0.5 ± 2.5 | 0.6 ± 2.3 | 0.4 ± 2 | 1.4 ± 4.9 | 5.5 ± 12.1 | -6.8 ± 13.4 |
| 5 | SSP370 | 2030_2060 | -0.4 ± 3.1 | 0.4 ± 2.3 | 0 ± 2.3 | -0.1 ± 1.7 | -0.1 ± 4.8 | 3.3 ± 11.1 | -3.2 ± 13.5 |
| 8 | SSP370 | 2070_2100 | 0.8 ± 2.8 | 0.5 ± 2.3 | 0.7 ± 2.1 | 0 ± 1.6 | 2 ± 4.5 | 4.5 ± 12.5 | -6.4 ± 13.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 2.9 | 0.5 ± 2.4 | 0.6 ± 2.3 | 0.4 ± 1.9 | 1.5 ± 4.8 | 4.7 ± 12 | -6.1 ± 13.4 |
| 9 | SSP585 | 2070_2100 | 1 ± 3.8 | 1.1 ± 3.7 | 1.5 ± 3.7 | 0.4 ± 1.9 | 4 ± 6.7 | 5.8 ± 14.5 | -9.8 ± 13.8 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 5.5 | 0.4 ± 9.2 | 1.4 ± 11.7 | 1.8 ± 15.8 | 0.1 ± 13.5 | -2 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 5.8 | 0 ± 10.7 | 0 ± 11 | 0 ± 16.5 | 0 ± 17 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.6 ± 5.9 | 1 ± 10.3 | 0.1 ± 10.8 | 1.7 ± 16 | -1.1 ± 16.6 | -0.6 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 3.5 ± 7.4 | 6.6 ± 11 | -0.8 ± 10 | 9.3 ± 16.6 | -7.3 ± 14.8 | -2 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 3.9 ± 6.9 | 8.4 ± 11.4 | -0.3 ± 11.1 | 12 ± 17.3 | -10.1 ± 14.8 | -2 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 4.2 ± 7.5 | 1.7 ± 10.4 | 0.3 ± 11.1 | 6.2 ± 17 | -4.2 ± 16.8 | -2 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 7.6 ± 6.9 | 5.8 ± 12.2 | -1.5 ± 9.4 | 15.9 ± 17.6 | -14 ± 16 | -2 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 4.6 ± 8.1 | 7.9 ± 11.8 | -0.6 ± 10.5 | 11.9 ± 17.8 | -9.9 ± 15.2 | -2 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 8.2 ± 9 | 11.5 ± 10.8 | -1.4 ± 11 | 20.3 ± 18 | -18.3 ± 14.9 | -2 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 1.6 | 0.7 ± 5.3 | 0.6 ± 3.5 | 1.7 ± 6.6 | 4.1 ± 22.2 | -5.8 ± 28.7 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 1.4 | 0 ± 4.8 | 0 ± 3 | 0 ± 5.8 | 0 ± 22.8 | 0 ± 28.8 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.2 ± 1.4 | -0.5 ± 4.5 | -0.1 ± 2.9 | -0.8 ± 5.5 | 1.1 ± 22.8 | -0.3 ± 28.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 1.3 | 1.4 ± 5 | 1.2 ± 2.8 | 3 ± 5.9 | 7.8 ± 22.3 | -10.8 ± 28.4 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.7 ± 1.5 | 1.2 ± 5.9 | 1.2 ± 3.1 | 3.1 ± 6.8 | 10.5 ± 21.6 | -13.6 ± 27.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.6 ± 1.4 | 0.8 ± 5.8 | 0.9 ± 3.3 | 2.3 ± 6.8 | 7.8 ± 22.5 | -10.2 ± 28.6 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 1.6 ± 1.5 | 2.5 ± 6.8 | 2.5 ± 4.3 | 6.6 ± 8.2 | 7.9 ± 21.5 | -14.6 ± 27.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.8 ± 1.5 | 1.6 ± 5 | 1 ± 3.2 | 3.4 ± 6.1 | 10.3 ± 22.6 | -13.7 ± 28.5 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 1.5 ± 2 | 2.2 ± 6.4 | 2.2 ± 3.6 | 5.9 ± 7.6 | 12.3 ± 20.6 | -18.1 ± 26.7 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -1.1 ± 7.4 | -2.9 ± 10.3 | -2 ± 13.5 | -1.1 ± 4.6 | -7.1 ± 19.1 | -2.6 ± 21.8 | 10 ± 7.5 |
| 2 | historical | 1970_2000 | 0 ± 7.8 | 0 ± 10.6 | 0 ± 14.6 | 0 ± 4.5 | 0 ± 20.1 | 0 ± 23.3 | 0 ± 10.6 |
| 3 | historical | 1985_2015 | 0.1 ± 8.1 | -0.2 ± 10.3 | -1.1 ± 12.9 | 0.4 ± 5.3 | -0.8 ± 19.1 | 0.3 ± 21.8 | 0.7 ± 10.3 |
| 4 | SSP245 | 2030_2060 | 2.9 ± 8.6 | 2.4 ± 11.4 | 3.4 ± 13.6 | -0.5 ± 4.7 | 8.2 ± 20.3 | -4.4 ± 23.1 | -3.6 ± 7.5 |
| 7 | SSP245 | 2070_2100 | 4.7 ± 7.9 | 2.1 ± 11.6 | 3.9 ± 13.1 | -0.4 ± 4.5 | 10.3 ± 19.7 | -5.6 ± 21.6 | -4.5 ± 7.5 |
| 5 | SSP370 | 2030_2060 | 3.3 ± 8.4 | 1.7 ± 11.3 | 2.7 ± 13.5 | -0.9 ± 4.3 | 6.8 ± 19.9 | -2.7 ± 21.6 | -4 ± 7.5 |
| 8 | SSP370 | 2070_2100 | 6.2 ± 8.3 | 0.8 ± 11.4 | 5 ± 14.1 | -1.2 ± 4.8 | 10.8 ± 20.5 | -6.3 ± 23.1 | -4.5 ± 7.5 |
| 6 | SSP585 | 2030_2060 | 4.1 ± 8.4 | 2.3 ± 11.2 | 4.2 ± 13.5 | -1.2 ± 4.4 | 9.4 ± 20 | -4.9 ± 21.7 | -4.3 ± 7.5 |
| 9 | SSP585 | 2070_2100 | 8.2 ± 7.9 | 2.6 ± 12.6 | 4.6 ± 13.5 | -1.9 ± 4.3 | 13.5 ± 20.6 | -8.9 ± 21.4 | -4.5 ± 7.5 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 0.3 | 1.5 ± 7.1 | 0.1 ± 3.5 | 1.6 ± 8 | 0.6 ± 13.3 | -2.3 ± 16 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0.3 | 0 ± 6.2 | 0 ± 3.4 | 0 ± 7.1 | 0 ± 13.9 | 0 ± 16.5 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.1 ± 0.3 | 0.3 ± 5.8 | -0.1 ± 3.3 | 0.1 ± 6.7 | -0.9 ± 14.2 | 0.7 ± 16.4 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0 ± 0.4 | 2.2 ± 7 | 0.5 ± 3.5 | 2.7 ± 7.9 | 3 ± 13.1 | -5.7 ± 16.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.1 ± 0.4 | 2.1 ± 6.7 | 0.9 ± 3.8 | 3.1 ± 7.7 | 5.2 ± 13.3 | -8.4 ± 16.1 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 1.6 ± 6.5 | 0.4 ± 3.6 | 2.1 ± 7.5 | 2.5 ± 13.8 | -4.7 ± 16.9 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.4 ± 0.7 | 3.3 ± 5.9 | 1 ± 3.1 | 4.8 ± 6.8 | 3.9 ± 14.4 | -8.8 ± 16.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 2.3 ± 6.8 | 1 ± 3.7 | 3.4 ± 7.8 | 3.7 ± 13.4 | -7.2 ± 16.5 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 0.6 ± 1.4 | 2.7 ± 7.5 | 1 ± 3.5 | 4.5 ± 8.4 | 7.6 ± 13.5 | -12 ± 16.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0 | 0.7 ± 37.1 | -0.1 ± 24.4 | -0.2 ± 4.4 | 0.5 ± 44.6 | -0.4 ± 17.9 | -0.2 ± 0.4 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 37.3 | 0 ± 24.6 | 0 ± 4.4 | 0 ± 44.9 | 0 ± 18.1 | 0 ± 0.6 |
| 3 | historical | 1985_2015 | 0 ± 0 | 2.1 ± 37.1 | -1.3 ± 25 | 0 ± 4.5 | 0.8 ± 45 | -0.9 ± 17.8 | 0.1 ± 0.5 |
| 4 | SSP245 | 2030_2060 | 5.2 ± 0 | 4.5 ± 35.3 | -5.1 ± 23 | -1.1 ± 4 | 3.5 ± 42.4 | -3.4 ± 17.5 | -0.2 ± 0.5 |
| 7 | SSP245 | 2070_2100 | 5.3 ± 0 | 6.6 ± 34.5 | -7.7 ± 21.8 | -0.6 ± 4.5 | 3.6 ± 41 | -3.1 ± 17.5 | -0.4 ± 0.4 |
| 5 | SSP370 | 2030_2060 | 5.5 ± 0 | 3.5 ± 34.8 | -3.4 ± 23.2 | -1 ± 4 | 4.6 ± 42 | -4.4 ± 15.1 | -0.2 ± 0.4 |
| 8 | SSP370 | 2070_2100 | 5.3 ± 10.2 | 6.8 ± 32.9 | -8.3 ± 22.2 | -1.1 ± 4.5 | 2.7 ± 41.3 | -2.5 ± 20.7 | -0.2 ± 0.5 |
| 6 | SSP585 | 2030_2060 | 0.4 ± 3.6 | 4.3 ± 36.6 | -3 ± 24.1 | -0.1 ± 4.8 | 1.6 ± 44.2 | -1.6 ± 18.8 | -0.2 ± 0.5 |
| 9 | SSP585 | 2070_2100 | 4.1 ± 6.4 | 8.1 ± 34.2 | -7.4 ± 21.7 | -1.3 ± 4 | 3.5 ± 41.2 | -3.1 ± 17.9 | -0.5 ± 0.4 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0.1 | 0.3 ± 2.1 | 1.3 ± 9.7 | 0.1 ± 3.5 | 1.7 ± 10.5 | -1.3 ± 13.8 | -0.4 ± 2.8 |
| 2 | historical | 1970_2000 | 0 ± 0.1 | 0 ± 1.8 | 0 ± 6.1 | 0 ± 3.3 | 0 ± 7.1 | 0 ± 10.3 | 0 ± 3.7 |
| 3 | historical | 1985_2015 | 0.1 ± 0.1 | 0.2 ± 1.8 | 0.6 ± 6.7 | 0.2 ± 3.3 | 1.1 ± 7.7 | -0.9 ± 10.6 | -0.2 ± 3.7 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 0.2 | 0.9 ± 2.6 | 3.5 ± 10.5 | 1.5 ± 4.3 | 6 ± 11.6 | -5.1 ± 15 | -1 ± 2.8 |
| 7 | SSP245 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 3.7 | 4.4 ± 10.1 | 2 ± 5.1 | 8 ± 11.9 | -7 ± 16.4 | -1 ± 2.7 |
| 5 | SSP370 | 2030_2060 | 0.1 ± 0.1 | 0.4 ± 2.1 | 2.4 ± 7 | 1.5 ± 4.4 | 4.4 ± 8.5 | -4 ± 11.5 | -0.4 ± 3.1 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 2.8 | 4.7 ± 8.2 | 1.4 ± 4.4 | 7.7 ± 9.8 | -7.2 ± 13 | -0.5 ± 3.2 |
| 6 | SSP585 | 2030_2060 | 0.1 ± 0.1 | 1.3 ± 2.8 | 4.7 ± 10 | 1.9 ± 5 | 8 ± 11.5 | -6.9 ± 15 | -1.1 ± 2.7 |
| 9 | SSP585 | 2070_2100 | 0.5 ± 1 | 2.6 ± 4 | 5.7 ± 10.4 | 2.4 ± 4.5 | 11.2 ± 12.1 | -10.1 ± 14.6 | -1.2 ± 2.7 |
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent.cont, "tab.percent.continent.txt")
tab.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup()
list_ref <- list()
for(i in unique(tab.cont$source)){
tab.cont.i <- filter(tab.cont, source == i)
tab.cont.i.ref <- filter(tab.cont.i, period == "1970_2000")
for(j in unique(tab.cont.i$Continent)){
tab.cont.i.j <- filter(tab.cont.i, Continent == j)
tab.cont.i.j.ref <- filter(tab.cont.i.ref, Continent == j)
for(k in unique(tab.cont.i$cat.AI)){
index <- paste(i, j, k, sep = "_")
tab.cont.i.j.k <- filter(tab.cont.i.j, cat.AI == k)
lines <- ifelse(dim(tab.cont.i.j.k)[1] > 0, dim(tab.cont.i.j.k)[1] - 1, 1)
tab.cont.i.j.k.ref <- subset(tab.cont.i.j.ref, cat.AI == k)
if(dim(tab.cont.i.j.k.ref)[1] == 0){
names1 <- names(tab.cont.i.j.k.ref)
line1 <- c("1970_2000","historical", i, j, k, 0)
tab.cont.i.j.k.ref <- rbind(tab.cont.i.j.k.ref, line1) %>%
setNames(names1)
}else{
tab.cont.i.j.k.ref <- tab.cont.i.j.k.ref
}
tab.cont.i.j.k.ref <- tab.cont.i.j.k.ref %>% rbind(tab.cont.i.j.k.ref[rep(1,lines),])
tab.cont.i.j.k$diff <- tab.cont.i.j.k$count - as.numeric(tab.cont.i.j.k.ref$count)
list_ref[[index]] <- tab.cont.i.j.k
}
}
}
tab.cont.diff <- bind_rows(list_ref, .id = "column_label") %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(diff.mean = mean(diff, na.rm = T), diff.sd = sd(diff, na.rm = T))
write.table(tab.cont.diff, "tab.cont.diff.txt")
tab.cont.diff <- read.table("tab.cont.diff.txt")
tab_list_percent <- list()
tab_list_sd <- list()
for(i in unique(tab.cont.diff$Continent)){
tab.i <- subset(tab.cont.diff, Continent == i)
df.percent <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "diff.mean")
df.sd <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "diff.sd")
# sd for a sum: square root of sum of squared sd
drycats <- which(names(df.percent) %in% c("Hyperarid","Arid","Semi-arid","Dry subhumid"))
df.percent <- df.percent %>% mutate("Sum drylands" = rowSums(.[drycats], na.rm = T))
df.sd <- df.sd %>% mutate("Sum drylands" = round(sqrt(rowSums((.[drycats])^2, na.rm = T)),2))
missing <- setdiff(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"), names(df.percent))
df.percent[, missing] <- 0
df.sd[,missing] <- 0
df.percent <- df.percent %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.percent[is.na(df.percent)] <- 0
tab_list_percent[[i]] <- df.percent
df.sd <- df.sd %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.sd[is.na(df.sd)] <- 0
tab_list_sd[[i]] <- df.sd
}
k_list <- list()
for(i in names(tab_list_percent)){
df <- tab_list_percent[[i]] %>% select(c("model", "period"))
df$Hyperarid <- paste(tab_list_percent[[i]]$Hyperarid, tab_list_sd[[i]]$Hyperarid, sep = " ± ")
df$Arid <- paste(tab_list_percent[[i]]$Arid, tab_list_sd[[i]]$Arid, sep = " ± ")
df$'Semi-Arid' <- paste(tab_list_percent[[i]][,5], tab_list_sd[[i]][,5], sep = " ± ")
df$'Dry subhumid' <- paste(tab_list_percent[[i]][,6], tab_list_sd[[i]][,6], sep = " ± ")
df$'Sum drylands' <- paste(tab_list_percent[[i]][,7], tab_list_sd[[i]][,7], sep = " ± ")
df$Humid <- paste(tab_list_percent[[i]]$Humid, tab_list_sd[[i]]$Humid, sep = " ± ")
df$Cold <- paste(tab_list_percent[[i]]$Cold, tab_list_sd[[i]]$Cold, sep = " ± ")
k_list[[i]] <- kable(df[order(df$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = colorvec[5]) %>% row_spec(2, bold = T)
}
k_list
$AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -5.84615384615385 ± 24.0584224816943 | 3 ± 22.7449628123093 | 4.76923076923077 ± 33.9562312547438 | -0.384615384615385 ± 11.9620125225542 | 1.53846153846153 ± 48.91 | 2.84615384615385 ± 38.6972138399441 | 0 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | -10.5384615384615 ± 19.5474439275974 | 11 ± 21.6024689946929 | 5.92307692307692 ± 11.9335768489693 | -3.53846153846154 ± 10.1457329669126 | 2.84615384615388 ± 33.08 | -2.69230769230769 ± 15.5155009339296 | -2 ± 0 |
| 4 | SSP245 | 2030_2060 | -10.4615384615385 ± 41.0479706859657 | 24.3076923076923 ± 29.7388203514772 | 25.8461538461538 ± 40.2778809311971 | 2.53846153846154 ± 15.4844835486764 | 42.2307692307691 ± 66.57 | -37.8461538461538 ± 53.1551912702265 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 10.0769230769231 ± 66.8324541153243 | 9 ± 46.1808040928985 | 42.0769230769231 ± 51.9157354733956 | -2.30769230769231 ± 22.6545970882461 | 58.8461538461539 ± 99.03 | -54.4615384615385 ± 61.6814604569523 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | -29.4615384615385 ± 88.4153223755319 | -5.23076923076923 ± 60.9455410539522 | 45.4615384615385 ± 86.1293749586587 | 12.9230769230769 ± 45.5621581623419 | 23.6923076923077 ± 145 | -21.3846153846154 ± 43.4291347322249 | -30 ± 0 |
| 8 | SSP370 | 2070_2100 | 19.4615384615385 ± 66.2578490754308 | 18.4615384615385 ± 58.2217247320039 | 31.0769230769231 ± 46.6341461779198 | 0.230769230769231 ± 27.395723042578 | 69.2307692307693 ± 103.47 | -67.0769230769231 ± 52.3425600228303 | -28 ± 0 |
| 6 | SSP585 | 2030_2060 | -7.84615384615385 ± 50.8524764291215 | 13.6153846153846 ± 39.557002038279 | 33.2307692307692 ± 47.566013507815 | 0 ± 18.4481254693623 | 39 ± 82.18 | -34.6153846153846 ± 52.663299145829 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 45.5833333333333 ± 209.609792594514 | 9.25 ± 79.7702667323199 | 16.5 ± 83.830456604658 | -3.83333333333333 ± 20.8232096614826 | 67.5 ± 240.33 | -62.75 ± 98.0381419281663 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 4.30769230769231 ± 28.0070961703893 | 8.76923076923077 ± 41.4993049061344 | 11.3076923076923 ± 61.526938023634 | 15.8461538461538 ± 39.580816384216 | 40.2307692307692 ± 88.65 | 90.2307692307692 ± 176.826258347073 | -130.461538461538 ± 287.741091777723 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | -5.15384615384615 ± 19.9743425166977 | -12.6153846153846 ± 41.9554097853472 | -17 ± 29.8161030317511 | -1.61538461538462 ± 17.1975698939243 | -36.3846153846154 ± 57.83 | -33.6153846153846 ± 169.076185225053 | 70 ± 234.12461069553 |
| 4 | SSP245 | 2030_2060 | -15.6923076923077 ± 38.0840837607712 | 24.2307692307692 ± 40.5301407312176 | 22.2307692307692 ± 88.9280175630398 | 27 ± 65.5222608482542 | 57.7692307692307 ± 123.67 | 192.692307692308 ± 202.871217202517 | -250.461538461538 ± 375.760920308072 |
| 7 | SSP245 | 2070_2100 | -8.46153846153846 ± 31.9103310977688 | 23.6153846153846 ± 37.8627751702083 | 30.0769230769231 ± 101.227023350537 | 22.8461538461538 ± 72.2286256201401 | 68.076923076923 ± 133.85 | 294.461538461538 ± 208.6546650108 | -362.538461538462 ± 381.791744145203 |
| 5 | SSP370 | 2030_2060 | -23.8461538461538 ± 61.3716087370566 | 21.6153846153846 ± 45.7156764898331 | 0 ± 55.9359753051052 | -2.30769230769231 ± 25.9627445113975 | -4.53846153846151 ± 98.28 | 174.230769230769 ± 120.104228239582 | -169.692307692308 ± 100.362164696484 |
| 8 | SSP370 | 2070_2100 | 40.0769230769231 ± 64.3343629517714 | 25.3076923076923 ± 63.8336178610516 | 32.6923076923077 ± 53.0383267323179 | 3.07692307692308 ± 29.0214332820347 | 101.153846153846 ± 108.94 | 241.153846153846 ± 179.945753934274 | -342.307692307692 ± 156.047740886876 |
| 6 | SSP585 | 2030_2060 | -1.84615384615385 ± 30.813541811586 | 23.8461538461538 ± 34.0632895109632 | 27.0769230769231 ± 91.2044055391163 | 23.8461538461538 ± 54.9087821661923 | 72.9230769230768 ± 115.94 | 250 ± 201.292490338479 | -322.923076923077 ± 358.702862533523 |
| 9 | SSP585 | 2070_2100 | 55.0833333333333 ± 178.360441156936 | 59.5 ± 145.979761984636 | 77.9166666666667 ± 202.755768142737 | 22.5 ± 61.9259088089806 | 215 ± 313.16 | 324.083333333333 ± 262.604596765602 | -539.083333333333 ± 416.369691136338 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | -0.307692307692308 ± 4.67946961710789 | -0.538461538461538 ± 17.9704172860815 | 3.30769230769231 ± 10.7964856580326 | 2.46153846153846 ± 21.48 | -2 ± 28.2105181330179 | 0 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 1.69230769230769 ± 4.02874288466877 | 2.53846153846154 ± 6.25320430679856 | -0.0769230769230769 ± 4.34859246311452 | 4.15384615384615 ± 8.62 | -4 ± 5.03322295684717 | -2 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 9.84615384615385 ± 11.1343773500973 | 17.3076923076923 ± 25.7079255463648 | -3.15384615384615 ± 10.0070487977738 | 24 ± 29.75 | -23.5384615384615 ± 26.7507488512483 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 11 ± 10.2875329080073 | 22.6153846153846 ± 29.2191103604543 | -1.69230769230769 ± 19.0367391088242 | 31.9230769230769 ± 36.36 | -31.4615384615385 ± 27.3484167263097 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 11.7692307692308 ± 12.3568728929413 | 3.30769230769231 ± 24.015219533262 | 0 ± 15.6258333111123 | 15.0769230769231 ± 31.2 | -14.6153846153846 ± 37.2615495775884 | 0 ± 0 |
| 8 | SSP370 | 2070_2100 | 12 ± 15.556349186104 | 23.2307692307692 ± 15.0839530967728 | 18.6923076923077 ± 29.4855462200963 | -3.69230769230769 ± 13.4001530798757 | 50.2307692307692 ± 38.97 | -39.6153846153846 ± 23.8381293363471 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 12.8461538461538 ± 14.0466256555691 | 21.0769230769231 ± 29.104585945808 | -2.46153846153846 ± 16.4195786822489 | 31.4615384615384 ± 36.25 | -31 ± 25.5049014897137 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 6 ± 7.07106781186548 | 25.1666666666667 ± 24.8845820588256 | 35.5833333333333 ± 28.5767175840541 | -6.16666666666667 ± 18.5709124137396 | 60.5833333333333 ± 42.79 | -55.0833333333333 ± 42.4273889525779 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 4.66666666666667 ± 11.211465992321 | 8.16666666666667 ± 13.2653570238308 | 7.84615384615385 ± 11.3346530151725 | 20.6794871794872 ± 20.74 | 50.3076923076923 ± 106.563271201811 | -70 ± 115.036950585453 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | -2.16666666666667 ± 9.81495457622364 | -5.83333333333333 ± 24.5683955643436 | -1.23076923076923 ± 8.48679215166962 | -9.23076923076923 ± 27.78 | 12.9230769230769 ± 48.5291347859914 | -4.30769230769231 ± 73.0506497979137 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 4.66666666666667 ± 7.41415517572279 | 17.5 ± 14.5695822614352 | 11.5 ± 9.56793888700459 | 33.6666666666667 ± 18.94 | 97.5384615384615 ± 125.997761477877 | -128.538461538462 ± 146.339568233507 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 8.58333333333333 ± 11.1555802044076 | 21.6153846153846 ± 19.9354728291826 | 14 ± 9.32737905308882 | 44.1987179487179 ± 24.68 | 123.076923076923 ± 120.805257569405 | -166.615384615385 ± 139.523318517932 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 7.16666666666667 ± 8.48349556313298 | 17.6153846153846 ± 11.2215452110249 | 10.3846153846154 ± 7.83728759987005 | 35.1666666666667 ± 16.1 | 90.5384615384615 ± 75.058438771195 | -125.153846153846 ± 73.5026599902413 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 19.25 ± 13.7452471124319 | 37.6923076923077 ± 28.0338623078847 | 30 ± 31.8093277724213 | 86.9423076923077 ± 44.57 | 92.2307692307692 ± 110.037988778235 | -177.692307692308 ± 82.173986775403 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 9.83333333333333 ± 8.20014781832768 | 19.0833333333333 ± 14.2028059540957 | 12.2307692307692 ± 8.41777728138339 | 41.1474358974358 ± 18.43 | 125.923076923077 ± 134.35987839782 | -164.846153846154 ± 144.399241776545 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 18.2727272727273 ± 18.9530520449394 | 37.4166666666667 ± 24.7403179715487 | 26.0833333333333 ± 24.6298353197659 | 81.7727272727273 ± 39.72 | 150.666666666667 ± 131.040821768245 | -230.916666666667 ± 148.445556798893 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -2.07692307692308 ± 5.1552810861216 | -1.15384615384615 ± 9.04476048923864 | 1.76923076923077 ± 14.8893353677156 | -0.384615384615385 ± 8.18065259762795 | -1.84615384615385 ± 19.92 | 1 ± 20.7163381577601 | 13 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0.538461538461538 ± 5.82545255202524 | 0.230769230769231 ± 8.23765587767384 | -4.15384615384615 ± 17.8505189179762 | 2 ± 13.1782649338472 | -1.38461538461538 ± 24.37 | 2.46153846153846 ± 16.7163362444017 | -6.5 ± 9.19238815542512 |
| 4 | SSP245 | 2030_2060 | 11.2307692307692 ± 8.13586551586908 | 7 ± 10.0249688278817 | 11.6153846153846 ± 19.8432963556061 | -3.30769230769231 ± 10.1438373359117 | 26.5384615384615 ± 25.76 | -22.2307692307692 ± 35.5109228410871 | -54 ± 0 |
| 7 | SSP245 | 2070_2100 | 19.3846153846154 ± 8.20100056434642 | 6.84615384615385 ± 11.3860013016434 | 14.9230769230769 ± 23.0704050624082 | -2.53846153846154 ± 10.6584503612187 | 38.6153846153846 ± 29.03 | -36.5 ± 35.8570393748183 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 14 ± 6.7700320038633 | 6.07692307692308 ± 9.92019437361267 | 10.5384615384615 ± 14.3097599829358 | -4.53846153846154 ± 8.04793332286189 | 26.076923076923 ± 20.34 | -23.0833333333333 ± 20.7121322958894 | -56 ± 0 |
| 8 | SSP370 | 2070_2100 | 25.3076923076923 ± 16.7101995568805 | -0.923076923076923 ± 23.063179668256 | 17.6923076923077 ± 22.1260955110499 | -6.53846153846154 ± 21.33313301188 | 35.5384615384615 ± 41.9 | -30.9230769230769 ± 28.5115810927815 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 16.9230769230769 ± 9.8696634395635 | 7.84615384615385 ± 10.1722347089693 | 16.5384615384615 ± 17.1736978381447 | -5.84615384615385 ± 13.0054229320321 | 35.4615384615384 ± 25.79 | -33.1666666666667 ± 32.0137281159003 | -57 ± 0 |
| 9 | SSP585 | 2070_2100 | 32.4166666666667 ± 12.273092026005 | 11.3333333333333 ± 19.90583894847 | 21.1666666666667 ± 22.0735684528448 | -8.66666666666667 ± 18.1174618598308 | 56.25 ± 36.91 | -55.5454545454545 ± 27.4494576863137 | 0 ± 0 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.375 ± 3.06768875307034 | 31.75 ± 66.2901679258422 | 4.92307692307692 ± 27.7172074665467 | 37.0480769230769 ± 71.92 | 21.6923076923077 ± 91.341287319759 | -56.0769230769231 ± 173.993036995958 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.333333333333333 ± 4.18330013267038 | -3 ± 32.2038958682161 | -2.69230769230769 ± 36.415163085782 | -5.35897435897436 ± 48.79 | -19.7692307692308 ± 70.0180379689808 | 25.0769230769231 ± 113.976943237409 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 3.36363636363636 ± 2.87307247638229 | 60 ± 104.176132903207 | 14.1538461538462 ± 34.693530025655 | 77.5174825174826 ± 109.84 | 82.1538461538462 ± 94.51705856074 | -159.153846153846 ± 214.335735919859 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 4.7 ± 3.05686840482943 | 56.9230769230769 ± 85.9946331062405 | 25.4615384615385 ± 49.6548342470556 | 87.0846153846154 ± 99.35 | 145.923076923077 ± 122.363162715515 | -231.923076923077 ± 215.561847868333 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 5.45454545454545 ± 6.89004552036685 | 44.4615384615385 ± 45.1582686865787 | 12.0769230769231 ± 33.2452040111591 | 61.9930069930071 ± 56.5 | 70.4615384615385 ± 48.5397009066039 | -131.615384615385 ± 77.0427786595846 |
| 8 | SSP370 | 2070_2100 | 4 ± 0 | 15.6923076923077 ± 20.0307455984736 | 91.5384615384615 ± 59.6093049009065 | 28.3846153846154 ± 49.8339550600098 | 139.615384615385 ± 80.24 | 108 ± 110.324521299664 | -243.923076923077 ± 105.99564577414 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 7.09090909090909 ± 7.86707754448303 | 62.0769230769231 ± 90.9537075829068 | 29.8461538461538 ± 46.234630155772 | 99.013986013986 ± 102.33 | 103.384615384615 ± 97.9187915754159 | -201.307692307692 ± 203.199157074771 |
| 9 | SSP585 | 2070_2100 | 5 ± 0 | 20.1818181818182 ± 38.4468937154048 | 91.1666666666667 ± 135.959107042812 | 30.75 ± 35.4481054757258 | 147.098484848485 ± 145.67 | 198.75 ± 99.7835156726801 | -339.583333333333 ± 220.157158054023 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 1 ± 0 | 6 ± 42.7668095606862 | -1.23076923076923 ± 38.0660694191775 | -1.23076923076923 ± 6.80874249468831 | 4.53846153846154 ± 57.66 | -3 ± 12.0623380818148 | 0.166666666666667 ± 1.16904519445001 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 1 ± 0 | 5.08333333333333 ± 32.8977985815944 | -2.23076923076923 ± 21.2922280271223 | 1.76923076923077 ± 9.42650382480028 | 5.62179487179487 ± 40.3 | -3 ± 7.85281265959316 | -0.25 ± 0.957427107756338 |
| 4 | SSP245 | 2030_2060 | 45 ± 0 | 50.1538461538462 ± 62.0991762611042 | -26.4615384615385 ± 39.9408216085903 | -5.84615384615385 ± 12.4354744839522 | 62.8461538461539 ± 74.87 | -19.8461538461538 ± 18.3296033501644 | -1.5 ± 0.707106781186548 |
| 7 | SSP245 | 2070_2100 | 45 ± 0 | 66.6153846153846 ± 81.5041292016734 | -48.4615384615385 ± 58.5172558376521 | -2.46153846153846 ± 13.0103509087661 | 60.6923076923076 ± 101.18 | -17.3846153846154 ± 18.8172724800845 | -5 ± 0 |
| 5 | SSP370 | 2030_2060 | 47 ± 0 | 42.3846153846154 ± 50.2535877020047 | -12.3846153846154 ± 33.3105050035232 | -5.30769230769231 ± 10.3068311925038 | 71.6923076923077 ± 61.17 | -27 ± 61.1882341631134 | -3.33333333333333 ± 3.21455025366432 |
| 8 | SSP370 | 2070_2100 | 70.6666666666667 ± 84.0376899571456 | 63.1538461538462 ± 110.812789690425 | -57 ± 62.8887907341205 | -6.84615384615385 ± 13.452671072109 | 69.974358974359 ± 153.22 | -14.1538461538462 ± 57.5135435554765 | -5 ± 0 |
| 6 | SSP585 | 2030_2060 | 23.5 ± 0.707106781186548 | 33.8461538461538 ± 55.8910340958878 | -23.2307692307692 ± 43.1164196220609 | -0.769230769230769 ± 12.2348807796524 | 33.3461538461538 ± 71.65 | -12 ± 20.0956048262632 | -1.5 ± 0.707106781186548 |
| 9 | SSP585 | 2070_2100 | 60.3333333333333 ± 48.6757982300582 | 69.8333333333333 ± 126.826892282594 | -50 ± 91.7645207731576 | -9 ± 14.1806526327567 | 71.1666666666666 ± 164.55 | -23.9166666666667 ± 22.0802599347897 | 0 ± 0 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -0.142857142857143 ± 0.377964473009227 | 5.07692307692308 ± 13.901208211648 | 20.0769230769231 ± 83.3551253557747 | 1.30769230769231 ± 18.8121619145019 | 26.3186813186813 ± 86.58 | -20.6153846153846 ± 79.5115698725019 | -6.81818181818182 ± 35.0679859182651 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0.5 ± 1.22474487139159 | 2.69230769230769 ± 5.87912430248779 | 10.5384615384615 ± 15.6504280272425 | 3 ± 9.31844049899624 | 16.7307692307692 ± 19.18 | -14.2307692307692 ± 13.1539211273917 | -2.54545454545455 ± 5.75088925929958 |
| 4 | SSP245 | 2030_2060 | 1.57142857142857 ± 1.13389341902768 | 13.1538461538462 ± 26.6328310982459 | 55.6153846153846 ± 93.1374776531432 | 23.4615384615385 ± 24.7810928754679 | 93.8021978021979 ± 100 | -78.4615384615385 ± 97.2167812885335 | -18.8 ± 42.023273974734 |
| 7 | SSP245 | 2070_2100 | 1.42857142857143 ± 0.975900072948533 | 22.6153846153846 ± 49.907478500285 | 68.9230769230769 ± 90.7647706422684 | 31 ± 41.3481156362254 | 123.967032967033 ± 111.53 | -107 ± 130.065368180773 | -22.7777777777778 ± 43.6886077192264 |
| 5 | SSP370 | 2030_2060 | 1.25 ± 0.886405260427918 | 5.92307692307692 ± 19.7250329043306 | 38.1538461538462 ± 31.8821950149979 | 24.0769230769231 ± 36.5637833984704 | 69.4038461538462 ± 52.38 | -59.4615384615385 ± 55.635143846037 | -12.8888888888889 ± 18.6711304186734 |
| 8 | SSP370 | 2070_2100 | 2 ± 2.26778683805536 | 22.9230769230769 ± 23.8168761541809 | 73.5384615384615 ± 82.0869613932032 | 22.6923076923077 ± 35.8849657827727 | 121.153846153846 ± 92.73 | -109.461538461538 ± 109.343964461248 | -15 ± 15.1492574075431 |
| 6 | SSP585 | 2030_2060 | 1.5 ± 1.0690449676497 | 19.2307692307692 ± 29.9197644992789 | 72.8461538461538 ± 84.535836733942 | 30.1538461538462 ± 38.093844983685 | 123.730769230769 ± 97.44 | -107.538461538462 ± 99.3576162024628 | -20.1 ± 41.1675438503036 |
| 9 | SSP585 | 2070_2100 | 7.375 ± 15.259072242908 | 40.5 ± 51.561966250681 | 96.9166666666667 ± 122.811354474571 | 38 ± 40.7274756488451 | 182.791666666667 ± 140.12 | -160.166666666667 ± 133.095204020781 | -29.375 ± 47.7850469737732 |
list_plots <- list()
for(i in names(tab_list_percent)){
df.percent <- tab_list_percent[[i]]
df.sd <- tab_list_sd[[i]]
df <- merge(df.percent, df.sd, by = c("model", "period"))
g <- ggplot(data = df)+geom_point(aes(x=period,y= get("Sum drylands.x"), col = model), shape = "\u2605", size = 5, position = position_dodge(width = 0.5))+
geom_errorbar(aes(x = period, ymin = get("Sum drylands.x")-get("Sum drylands.y"), ymax = get("Sum drylands.x")+get("Sum drylands.y"), col = model), position = position_dodge(width = 0.5))+
scale_color_manual(values = colorvec[c(1,3,9,11)])+
ylim(0,100)+
labs(x="", y = "Percent of drylands", title = i)+
theme_minimal()
list_plots[[i]] <- g
}
ggarrange(plotlist = list_plots, ncol = 2, nrow = 4, common.legend = T, legend = "bottom")
tab.flow <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model, Continent) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
df245 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP245")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
tab.percent.region <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, Name, AI) %>%
summarise(AI.mean = mean(AI), AI.sd= sd(AI)) %>%
ungroup()
ggplot(subset(tab.percent.region, Continent == "AFRICA"))+
geom_point(aes(x=period, y = AI.mean, col = Name), position = "jitter", alpha = 0.5)
quantile(cmip6s$t.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## -53.4587313 -42.5828261 -28.4644864 -17.7502317 -7.2080554 -0.2580962
## 60% 70% 80% 90% 100%
## 7.1198406 15.3318920 22.8072968 26.4971129 34.3727997
t.breaks <- c(-50,-40,-30,-20,-10,0,10,20,30)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Mean annual temperature, °C")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Mean annual temperature, °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
#labels = c("2030-2060", "2070-2100"
quantile(cmip6s$diff.t.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50% 60%
## -3.5775507 -0.5616741 0.0000000 1.1670755 1.8544034 2.2566573 2.6600679
## 70% 80% 90% 100%
## 3.1444949 3.7454555 4.5875606 10.7627615
t.breaks <- c(-3,-0.5,0,1.5,2,3,4,5,10)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.t.mean))+
borders(colour = "grey60")+
scale_fill_gradient2(low = "#3182bd",mid = "white", high = "#b2182b", midpoint = 0)+
labs(title = i, fill = "Temperature anomalies compared to 1970-2000, °C")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right", legend.text = element_text(angle = 0))
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.t.mean))+
borders(colour = "grey60")+
scale_fill_gradient2(low = "#3182bd",mid = "white", high = "#b2182b", midpoint = 0)+
labs(fill = "Temperature anomalies compared to 1970-2000, °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
quantile(cmip6s$pr.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## 8.579696 55.000319 104.132310 200.852004 337.042710 463.946055
## 60% 70% 80% 90% 100%
## 579.741473 712.099280 959.490392 1431.315602 6863.723388
p.breaks <- c(10,50,100,200,300,400,500,600,700)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")
map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Annual mean\nprecipitation, mm")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Annual mean\nprecipitation, mm", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
cmip6s$pr.percent <- with(cmip6s, diff.pr.mean/spr.ref.mean*100)
quantile(cmip6s$pr.percent, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## -42.02999493 -2.87352237 0.00000000 0.04663897 2.46136429 5.19906939
## 60% 70% 80% 90% 100%
## 9.34748067 13.67239987 18.11758968 24.02085190 148.15226168
p.breaks <- c(-43,-3,0,3,5,9,13,18,24,148)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")
map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_gradient2(high = "#3182bd",mid = "white", low = "#b2182b", midpoint = 0)+
labs(title = i, fill = "Precipitation anomalies in %")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right", legend.text = element_text(angle = 0))
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_gradient2(high = "#3182bd", mid = "white", low = "#b2182b", midpoint = 0)+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_binned_divergingx(mid = 0, palette = "RdBu", rev = F, n_interp = 15)+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")